Noisy channel models, widely used in modern spellers, cope with typical misspellings, but do not work well with infrequent and difficult spelling errors. In this paper, we have improved the noisy channel approach by iterative stochastic search for the best correction. The proposed algorithm allowed us to avoid local minima problem and improve the F1 measure by 6.6% on distant spelling errors.

In today's marine habitats, anthropogenic noise is widespread in space and time, affecting aquatic animal communities. Short-term exposure to known affect vital behaviours, such as the ability evade predators. However, long-term pollution may lead differences short-term responses between naïve experienced animals. We investigated interaction sound on antipredator response of free-ranging sand g...

Seismic modeling aids the geophysicists to have a better understanding of the subsurface image before the seismic acquisition, processing, and interpretation. In this regard, seismic survey modeling is employed to make a model close to the real structure and to obtain very realistic synthetic seismic data. The objective of this study is to analyze the resolution and illumination of the fault by...

The paper presents an innovative approach for structural health monitoring of metallic components under fatigue crack phenomena. methodology is based on the evaluation information entropy acoustic emission (AE) data. AE testing growth (FCG) performed within extremely noisy environment. Basic data analysis demonstrated to be inefficient with regard specific conditions. proven a reliable damage-s...

To model printing noise a binary noisy channel and a set of controlled gates are introduced. The channel input is an image created by a halftoning algorithm and its output is the printed picture. Using this channel robustness to noise between halftoning algorithms can be studied. We introduced relative entropy to describe immunity of the algorithm to noise and tested several halftoning algorithms.

We offer a noisy channel approach for recognizing and correcting erroneous words in referring expressions. Our mechanism handles three types of errors: it removes noisy input, inserts missing prepositions, and replaces mis-heard words (at present, they are replaced by generic words). Our mechanism was evaluated on a corpus of 295 spoken referring expressions, improving interpretation performance.

We consider a scenario where an attacker is able to receive a stegosignal only over a noisy channel under the condition that he (or she) may even know the cover message (CM). We propose to use a spread-spectrum-signal-based stegosystem (SG) and prove that the capacity of such SG is zero for any fixed security level. Moreover, we show that the code rate satisfies a square root law due to Ker et ...

Natural language interfaces designed for situationally embedded domains (e.g. cars, videogames) must incorporate knowledge about the users’ context to address the many ambiguities of situated language use. We introduce a model of situated language acquisition that operates in two phases. First, intentional context is represented and inferred from user actions using probabilistic context free gr...

In this paper, we study learning visual classifiers from unstructured text descriptions at part precision with no training images. We propose a learning framework that is able to connect text terms to its relevant parts and suppress connections to non-visual text terms without any part-text annotations. For instance, this learning process enables terms like “beak” to be sparsely linked to the v...