A Smart Approach for GPT Cryptosystem Based on Rank Codes

The concept of Public- key cryptosystem was innovated by McEliece's cryptosystem. The public key cryptosystem based on rank codes was presented in 1991 by Gabidulin -Paramonov-Trejtakov(GPT). The use of rank codes in cryptographic applications is advantageous since it is practically impossible to utilize combinatoric decoding. This has enabled using public keys of a smaller size. Respective structural attacks against this system were proposed by Gibson and recently by Overbeck. Overbeck's attacks break many versions of the GPT cryptosystem and are turned out to be either polynomial or exponential depending on parameters of the cryptosystem. In this paper, we introduce a new approach, called the Smart approach, which is based on a proper choice of the distortion matrix X. The Smart approach allows for withstanding all known attacks even if the column scrambler matrix P over the base field Fq.Comment: 5 pages. to appear in Proceedings of IEEE ISIT201

On improving security of GPT cryptosystems

The public key cryptosystem based on rank error correcting codes (the GPT cryptosystem) was proposed in 1991. Use of rank codes in cryptographic applications is advantageous since it is practically impossible to utilize combinatoric decoding. This enabled using public keys of a smaller size. Several attacks against this system were published, including Gibson's attacks and more recently Overbeck's attacks. A few modifications were proposed withstanding Gibson's attack but at least one of them was broken by the stronger attacks by Overbeck. A tool to prevent Overbeck's attack is presented in [12]. In this paper, we apply this approach to other variants of the GPT cryptosystem.Comment: 5 pages. submitted ISIT 2009.Processed on IEEE ISIT201

3D Camouflaging Object using RGB-D Sensors

This paper proposes a new optical camouflage system that uses RGB-D cameras, for acquiring point cloud of background scene, and tracking observers eyes. This system enables a user to conceal an object located behind a display that surrounded by 3D objects. If we considered here the tracked point of observer s eyes is a light source, the system will work on estimating shadow shape of the display device that falls on the objects in background. The system uses the 3d observer s eyes and the locations of display corners to predict their shadow points which have nearest neighbors in the constructed point cloud of background scene.Comment: 6 pages, 12 figures, 2017 IEEE International Conference on SM

On the Ishikawa iteration process in Hilbert spaces

In this paper, we shall prove that a certain sequence of points which is iteratively defined converges always to a fixed point of some contractive mappings. The results generalize corresponding theorems of Singh and Qihou

THE COMPARABLY ALMOST (S,T)- STABILITY FOR RANDOM JUNGCK-TYPE ITERATIVE SCHEMES

The purpose of this paper is to introduce the concept of generalized - weakly con-tractive random operators and study a new concept of stability introduced by Kim [15] which is alled comparably almost stability and then prove the comparably almost (S,T)- stability for the  Jungck-type random iterative schemes. Our results extend, improve and unify the recent results in  [15], [19], [32] and many others. We also give stochastic version of many important known results

Random Fixed Points For Occasionally Weakly Compatible Mappings

In this paper, we obtain common random fixed point theorems for two pairs of occasionally weakly compatible self random mappings under contractive conditions involving two generalized altering distance functions in a complete separable metric space. Keywords: Common random fixed point, Complete separable metric space, Generalized altering distance function, Occasionally weakly compatible mappings, Point of coincidence

Retinopathy of prematurity in infants with birth weight above 1500 grams

Objective: To identify the rate and prognosis of retinopathy of prematurity (ROP) among newborn infants of birthweight of above 1500 grams, and the possible risk factors associated with the disease.Design: A prospective cohort study.Setting: Neonatal unit at Maternity Hospital, Kuwait city, Kuwait.Methods: All low birth weight infants were examined for the presence of ROP in the period between January 1996 to December 1997. Prospective collection of data on babies who were above 1500 grams was done to find an association between the disease in these babies and some of the maternal and neonatal risk factors.Results: A total of 68 babies of birth weight above 1500 grams were screened for ROP out of which 13 (19.1%) had different stages of the disease. None of the patients had threshold disease requiring surgery. Among the risk factors chosen, oxygen therapy, presence ofhypotension at birth and the non-use of surfactant were the only risk factors to be associated with disease. However, with logistic regression analysis, none of these were independently associated with ROP.Conclusion: ROP may occur in newborn infants of larger birthweight but with good prognosis, and oxygen therapy seems to predispose to the disease

CuisineNet: Food Attributes Classification using Multi-scale Convolution Network

Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.Comment: 8 pages, Submitted in CCIA 201

The Effect of Barium Content on the Crystallization and Microhardness of Barium Fluormica Glass-Ceramics

Mica glass-ceramics are easily machined due to their “House-of-Cards” microstructure. Barium fluormica glass-ceramics were developed and indicated good mechanical properties. This work studies the effect of varying barium content on the crystallization and microhardness of mica glass-ceramics. Four glasses were produced with different baria contents, then converted to mica glass-ceramics using a two-step heat treatment. They were characterized using Differential Scanning Calorimetry [DSC], X-ray Diffraction [XRD], Scanning electron microscopy [SEM] and Vickers microhardness. DSC showed some formulations indicated bulk crystallisation as the dominant mechanism. XRD showed the crystallization of Barium fluorphlogopite in all the compositions with minor secondary phases. SEM showed the formation of “House-of-Cards” microstructures and with an increase in BaO content, a decrease in contrast was observed in back scattered mode. Exceptionally low hardness values (<2 GPa) were obtained for longer heat treatments/holding times and are related to the well-developed house-of-cards microstructures formed

Predicting heart disease using modified GoogLeNet convolutional neural network architecture based on the heart sound

Based on the data of the world health organization (WHO), diagnosing heart disease is a great task, as heart disease (HD) is the most prevalent disease worldwide. We suggested a method based on heart sounds to deal with this difficult issue because the heart sound (HS) is an essential component for detecting heart conditions. A feature extraction technique and a classifier are used in the suggested strategy. We use the GoogLeNet convolutional neural network (CNN) architecture with some modifications to separate the most crucial attributes of HS, and the heart condition is classified as diseased or not diseased based on these attributes. The model is trained using the AdaBelief optimizer to tune the parameters of our modified GoogLeNet architecture. The model was trained and validated utilising various datasets from PhysioNet 2016. Additional training samples were provided by integrating the PASCAL dataset with the PhysioNet 2016 dataset. Additionally, the variety of samples from various sources enabled our system to learn about sounds from everyday life more accurately. Our results indicated that using a modified GoogLeNet architecture with the AdaBelief optimizer, the trained model obtained test accuracy of 100% and 99.9% on unseen HS recordings from PhysioNet and the merged datasets, respectively. By comparing our proposed model with the highest-scoring methods listed on the official PhysioNet website in these datasets, the results show significantly improved