Cognitive Reserve and Its Effect in Older Adults on Retrieval of Proper Names, Logo Names and Common Nouns

Previous studies showed that high Cognitive Reserve (CR, years of education and experience and knowledge acquired in life) is correlated with language proficiency as measured with vocabulary size, verbal analogy, and semantic processing. The aim of the present study is to investigate the relationship between CR and the ability in retrieving different categories of words: Proper Names, Logo Names, and Common Nouns. The hypothesis is that CR contributes more in retrieving Common Nouns and Logo Names which are highly semantically interconnected, than retrieving Proper Names which are pure referring expressions. Forty-six Italian healthy older adults underwent the Montreal Cognitive Assessment (MoCA) and their performances spanned from low to high global cognitive profile. They were also administered a picture naming task for Proper Names, Logo Names and Common Nouns. Latency and Accuracy were recorded. CR was measured with the Cognitive Reserve Index (CRI) questionnaire which provides a measure of education, working time activities, and leisure time activities. Participants were significantly faster and more accurate in name retrieval when CR was high. CRI and MoCA as interaction terms predicted naming Latency with a stronger effect of CRI when the global cognitive profile was in the low range. The effect of CRI on Accuracy was lower for Proper Names than for Common Nouns and Logo Names, which did not differ from each other. Our results show that name retrieval Accuracy can be predicted by CR, significantly more in the case of Logo Names and Common Nouns than in the case of Proper Names. As Proper Names have scarce semantic interconnections and are arbitrarily assigned to unique individuals, they are not much influenced by CR. Although Logo Names are also arbitrarily assigned to their bearers, they can be conceptually categorized and thus influenced by reserve. The weak relationship between Proper Names and CR might suggest a proper name task as a useful tool to detect early signs of dementia, in particular for persons with high CR

Face image super-resolution using 2D CCA

In this paper a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high-frequency components to the reconstructed high-resolution face. Unlike most of the previous researches on face super-resolution algorithms that first transform the images into vectors, in our approach the relationship between the high-resolution and the low-resolution face image are maintained in their original 2D representation. In addition, rather than approximating the entire face, different parts of a face image are super-resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms using multiple evaluation criteria including face recognition performance. Results on publicly available datasets show that the proposed method super-resolves high quality face images which are very close to the ground-truth and performance gain is not dataset dependent. The method is very efficient in both the training and testing phases compared to the other approaches. © 2013 Elsevier B.V

Towards Optimal Copyright Protection Using Neural Networks Based Digital Image Watermarking

In the field of digital watermarking, digital image watermarking for copyright protection has attracted a lot of attention in the research community. Digital watermarking contains varies techniques for protecting the digital content. Among all those techniques,Discrete Wavelet Transform (DWT) provides higher image imperceptibility and robustness. Over the years, researchers have been designing watermarking techniques with robustness in mind, in order for the watermark to be resistant against any image processing techniques. Furthermore, the requirements of a good watermarking technique includes a tradeoff between robustness, image quality (imperceptibility) and capacity. In this paper, we have done an extensive literature review for the existing DWT techniques and those combined with other techniques such as Neural Networks. In addition to that, we have discuss the contribution of Neural Networks in copyright protection. Finally we reached our goal in which we identified the research gaps existed in the current watermarking schemes. So that, it will be easily to obtain an optimal techniques to make the watermark object robust to attacks while maintaining the imperceptibility to enhance the copyright protection

Contextual Advertising Based on Content Recognition in a Video

Generally, the present disclosure is directed to providing relevant advertisements based on the visual content of a video. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to determine a relevant advertisement and/or a relevant time for the relevant advertisement based on image data taken from a video

Application of Pattern Recognition Techniques to the Classification of Full-Term and Preterm Infant Cry

Objectives: Scientific and clinical advances in perinatology and neonatology have enhanced the chances of survival of preterm and very low weight neonates. Infant cry analysis is a suitable noninvasive complementary tool to assess the neurologic state of infants particularly important in the case of preterm neonates. This article aims at exploiting differences between full-term and preterm infant cry with robust automatic acoustical analysis and data mining techniques. Study design: Twenty-two acoustical parameters are estimated in more than 3000 cry units from cry recordings of 28 full-term and 10 preterm newborns. Methods: Feature extraction is performed through the BioVoice dedicated software tool, developed at the Biomedical Engineering Lab, University of Firenze, Italy. Classification and pattern recognition is based on genetic algorithms for the selection of the best attributes. Training is performed comparing four classifiers: Logistic Curve, Multilayer Perceptron, Support Vector Machine, and Random Forest and three different testing options: full training set, 10-fold cross-validation, and 66% split. Results: Results show that the best feature set is made up by 10 parameters capable to assess differences between preterm and full-term newborns with about 87% of accuracy. Best results are obtained with the Random Forest method (receiver operating characteristic area, 0.94). Conclusions: These 10 cry features might convey important additional information to assist the clinical specialist in the diagnosis and follow-up of possible delays or disorders in the neurologic development due to premature birth in this extremely vulnerable population of patients. The proposed approach is a first step toward an automatic infant cry recognition system for fast and proper identification of risk in preterm babies