Energy Field as a Novel Approach to Challenge Viruses

Non

Iranian cashes recognition using mobile

In economical societies of today, using cash is an inseparable aspect of human life. People use cashes for marketing, services, entertainments, bank operations and so on. This huge amount of contact with cash and the necessity of knowing the monetary value of it caused one of the most challenging problems for visually impaired people. In this paper we propose a mobile phone based approach to identify monetary value of a picture taken from cashes using some image processing and machine vision techniques. While the developed approach is very fast, it can recognize the value of cash by average accuracy of about 95% and can overcome different challenges like rotation, scaling, collision, illumination changes, perspective, and some others.Comment: arXiv #13370

Memory- and time-efficient dense network for single-image super-resolution

Abstract Dense connections in convolutional neural networks (CNNs), which connect each layer to every other layer, can compensate for mid/high‐frequency information loss and further enhance high‐frequency signals. However, dense CNNs suffer from high memory usage due to the accumulation of concatenating feature‐maps stored in memory. To overcome this problem, a two‐step approach is proposed that learns the representative concatenating feature‐maps. Specifically, a convolutional layer with many more filters is used before concatenating layers to learn richer feature‐maps. Therefore, the irrelevant and redundant feature‐maps are discarded in the concatenating layers. The proposed method results in 24% and 6% less memory usage and test time, respectively, in comparison to single‐image super‐resolution (SISR) with the basic dense block. It also improves the peak signal‐to‐noise ratio by 0.24 dB. Moreover, the proposed method, while producing competitive results, decreases the number of filters in concatenating layers by at least a factor of 2 and reduces the memory consumption and test time by 40% and 12%, respectively. These results suggest that the proposed approach is a more practical method for SISR

Investigating the Relationship between Classification Quality and SMT Performance in Discriminative Reordering Models

Reordering is one of the most important factors affecting the quality of the output in statistical machine translation (SMT). A considerable number of approaches that proposed addressing the reordering problem are discriminative reordering models (DRM). The core component of the DRMs is a classifier which tries to predict the correct word order of the sentence. Unfortunately, the relationship between classification quality and ultimate SMT performance has not been investigated to date. Understanding this relationship will allow researchers to select the classifier that results in the best possible MT quality. It might be assumed that there is a monotonic relationship between classification quality and SMT performance, i.e., any improvement in classification performance will be monotonically reflected in overall SMT quality. In this paper, we experimentally show that this assumption does not always hold, i.e., an improvement in classification performance might actually degrade the quality of an SMT system, from the point of view of MT automatic evaluation metrics. However, we show that if the improvement in the classification performance is high enough, we can expect the SMT quality to improve as well. In addition to this, we show that there is a negative relationship between classification accuracy and SMT performance in imbalanced parallel corpora. For these types of corpora, we provide evidence that, for the evaluation of the classifier, macro-averaged metrics such as macro-averaged F-measure are better suited than accuracy, the metric commonly used to date

Towards efficient texture classification and abnormality detection

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