scholarly journals Unsupervised estimation for noisy-channel models

Author(s):  
Markos Mylonakis ◽  
Khalil Sima'an ◽  
Rebecca Hwa
Keyword(s):  
2021 ◽  
Author(s):  
Dario Paape ◽  
Shravan Vasishth ◽  
Ralf Engbert

Local coherence effects arise when the human sentence processor is temporarily misled by a locally grammatical but globally ungrammatical analysis ("The coach smiled at THE PLAYER TOSSED A FRISBEE by the opposing team"). It has been suggested that such effects occur either because sentence processing occurs in a bottom-up, self-organized manner rather than being under constant grammatical supervision (Tabor, Galantucci, & Richardson, 2004), or because local coherence can disrupt processing due to readers maintaining uncertainty about previous input (Levy, 2008). We report the results of an eye-tracking study in which subjects read German grammatical and ungrammatical sentences that either contained a locally coherent substring or not and gave binary grammaticality judgments. In our data, local coherence affected on-line processing immediately at the point of the manipulation. There was, however, no indication that local coherence led to illusions of grammaticality (a prediction of self-organization), and only weak, inconclusive support for local coherence leading to targeted regressions to critical context words (a prediction of the uncertain-input approach). We discuss implications for self-organized and noisy-channel models of local coherence.


Open Mind ◽  
2021 ◽  
pp. 1-17
Author(s):  
Dario Paape ◽  
Shravan Vasishth ◽  
Ralf Engbert

Abstract Local coherence effects arise when the human sentence processor is temporarily misled by a locally grammatical but globally ungrammatical analysis (The coach smiled at the player tossed a frisbee by the opposing team). It has been suggested that such effects occur either because sentence processing occurs in a bottom-up, self-organized manner rather than under constant grammatical supervision (Tabor et al., 2004), or because local coherence can disrupt processing due to readers maintaining uncertainty about previous input (Levy, 2008b). We report the results of an eye-tracking study in which subjects read German grammatical and ungrammatical sentences that either contained a locally coherent substring or not and gave binary grammaticality judgments. In our data, local coherence affected on-line processing immediately at the point of the manipulation. There was, however, no indication that local coherence led to illusions of grammaticality (a prediction of self-organization), and only weak, inconclusive support for local coherence leading to targeted regressions to critical context words (a prediction of the uncertain-input approach). We discuss implications for self-organized and noisy-channel models of local coherence.


2020 ◽  
Vol 2020 (4) ◽  
pp. 76-1-76-7
Author(s):  
Swaroop Shankar Prasad ◽  
Ofer Hadar ◽  
Ilia Polian

Image steganography can have legitimate uses, for example, augmenting an image with a watermark for copyright reasons, but can also be utilized for malicious purposes. We investigate the detection of malicious steganography using neural networkbased classification when images are transmitted through a noisy channel. Noise makes detection harder because the classifier must not only detect perturbations in the image but also decide whether they are due to the malicious steganographic modifications or due to natural noise. Our results show that reliable detection is possible even for state-of-the-art steganographic algorithms that insert stego bits not affecting an image’s visual quality. The detection accuracy is high (above 85%) if the payload, or the amount of the steganographic content in an image, exceeds a certain threshold. At the same time, noise critically affects the steganographic information being transmitted, both through desynchronization (destruction of information which bits of the image contain steganographic information) and by flipping these bits themselves. This will force the adversary to use a redundant encoding with a substantial number of error-correction bits for reliable transmission, making detection feasible even for small payloads.


1983 ◽  
Author(s):  
D. L. Neuhoff ◽  
P. C. Shields
Keyword(s):  

2020 ◽  
Vol 14 ◽  
Author(s):  
Keerti Tiwari

: Multiple-input multiple-output (MIMO) systems have been endorsed to enable future wireless communication requirements. The efficient system designing appeals an appropriate channel model, that considers all the dominating effects of wireless environment. Therefore, some complex or less analytically acquiescent composite channel models have been proposed typically for single-input single-output (SISO) systems. These models are explicitly employed for mobile applications, though, we need a specific study of a model for MIMO system which can deal with radar clutters and different indoor/outdoor and mobile communication environments. Subsequently, the performance enhancement of MIMO system is also required in such scenario. The system performance enhancement can be examined by low error rate and high capacity using spatial diversity and spatial multiplexing respectively. Furthermore, for a more feasible and practical system modeling, we require a generalized noise model along with a composite channel model. Thus, all the patents related to MIMO channel models are revised to achieve the near optimal system performance in real world scenario. This review paper offers the methods to improve MIMO system performance in less and severe fading as well as shadowing environment and focused on a composite Weibull-gamma fading model. The development is the collective effects of selecting the appropriate channel models, spatial multiplexing/detection and spatial diversity techniques both at the transmitter and the receivers in the presence of arbitrary noise.


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