Key-dependent side-channel cube attack on CRAFT

CRAFT is a tweakable block cipher introduced in 2019 that aims to provide strong protection against differential fault analysis. In this paper, we show that CRAFT is vulnerable to side-channel cube attacks. We apply side-channel cube attacks to CRAFT with the Hamming weight leakage assumption. We found that the first half of the secret key can be recovered from the Hamming weight leakage after the first round. Next, using the recovered key bits, we continue our attack to recover the second half of the secret key. We show that the set of equations that are solvable varies depending on the value of the key bits. Our result shows that 99.90% of the key space can be fully recovered within a practical time.     

Structural Characterization of Different Asphaltenes of Kuwaiti Origin

Asphaltenes obtained by precipitation from crude Kuwaiti oils have been analyzed by proton magnetic resonance (¹H-NMR), carbon-13 nuclear magnetic resonance (¹³C-NMR) and Infrared (IR) spectral techniques. The molecular weight and elemental analysis were also determined. These combined analytical data were used for the characterization of these Kuwaiti oils. The asphaltenes molecular weights range from approximately 4200-6500 with an H/C ratio of 0.91-1.1 with an average 45-71% aromatic carbons. The average side chain length was of 4-6 carbons. It can also be concluded that the asphaltenes under investigation contain 5-9 sets of condensed aromatic rings joined together by bridges of alkyl chains or other hetero atoms and the average number of each of these sets of condensed aromatic rings is nearly 7. There are a number of alicyclic rings and condensed alicyclic rings in asphaltene. The IR spectra showed main molecular groups including OH, NH, SH, C=O and aliphatic and aromatic C-H's.     

Thermal and hydraulic performance of a modified two-stage evaporative cooler

The thermal and hydraulic performance of a modified two-stage evaporative cooler is evaluated. Variables considered are the mode of operation, packing thickness, mass flow rate of the water flowing to the precooler, and the mass flux of water flowing over the packing media. The effectiveness of the system increased with the increase of the mass flow rate of water flowing to the precooler, decreasing the mass flux of water flowing to the packing, and with the increase of the packing thickness. The effectiveness of the system with structured packing was higher than that with sheathy leaf base or natural fiber packing.The air-side pressure drop per unit length in the direction of air flow was nearly constant when the structured packing was used. For the sheathy leaf and natural fiber packings, the air pressure drop increased at a uniform rate as the mass flux of water flowing over the packing increased. The air pressure drop was lowest for the setup with the structured packing.     

Harmonic and intermodulation performance in an R-LED series network

Analytical expressions are obtained for predicting the harmonic and intermodulation performance of R-LED series networks. These expressions are in terms of the ordinary Bessel functions with arguments depenedent on the modulation index.     

Optimization Models for Layout and Pipe Design for Storm Sewer Systems

Optimization models are developed for simultaneously determining the pipe layout and the pipe design for storm sewer systems. The pipe design process includes computation of commercial diameters, slopes, and crown elevations for the storm sewer pipes. The optimization models aim to minimize the total costs of the layout and the pipe design for most of system elements. The optimization models are formulated as a 0–1 Integer Nonlinear Programming problem and solved using the General Algebraic Modeling System without the use of heuristic models which were characteristic of all previous models for the simultaneous determine of the pipe layout and pipe design of sewer networks. The models are based upon two different optimization approaches: (1) considers one or more commercial diameters of pipe connecting two manholes and (2) considers only one commercial diameter in a pipe connecting two manholes. The commercial diameters, pipe slopes, crown elevations, and total costs of the storm sewer system were compared for the two approaches using an example that illustrates the savings in cost by allowing multiple pipe sizes. The two new optimization modeling approaches developed herein can simultaneously determine the minimum cost pipe design (commercial diameters, slopes, and crown elevations) and pipe layout of storm sewer systems and satisfy all design constraints.

     

Gurum Seeds: A Potential Source of Edible Oil

Cucurbitaceae family seeds are mostly discarded as agro-industrial wastes. Gurum (Citrullus lanatus var. colocynthoide) is an underutilized wild cucurbit plant, closely related to desert watermelon, which is grown abundantly in some African countries. Gurum seeds can play a significant role in health and nutrition due to their high oil content. This review describes the nutritional composition of gurum seeds and their oil profile. Gurum seeds are a good source of oil (27–35.5%), fiber (26–31%), crude protein (15–18%), and carbohydrates (14–17%). Gurum seeds oil is extracted by supercritical CO₂ (SFE), screw press, and solvent extraction techniques. The gurum seeds oil is composed of unsaturated fatty acids with a high proportion of linoleic acid (C18:2) and oleic acid (C18:1). Gurum seeds oil contains various bioactive compounds, such as tocopherols, phytosterols, and polyphenols. It is reported that solvent extraction gives a higher yield than the screw press and SFE, but the SFE is preferred due to safety issues. More studies are required for producing better quality gurum seeds oil by using novel extraction techniques that can increase oil yield.     

Performance analysis of support vector machines classifiers in breast cancer mammography recognition

Support vector machine (SVM) is a supervised machine learning approach that was recognized as a statistical learning apotheosis for the small-sample database. SVM has shown its excellent learning and generalization ability and has been extensively employed in many areas. This paper presents a performance analysis of six types of SVMs for the diagnosis of the classical Wisconsin breast cancer problem from a statistical point of view. The classification performance of standard SVM (St-SVM) is analyzed and compared with those of the other modified classifiers such as proximal support vector machine (PSVM) classifiers, Lagrangian support vector machines (LSVM), finite Newton method for Lagrangian support vector machine (NSVM), Linear programming support vector machines (LPSVM), and smooth support vector machine (SSVM). The experimental results reveal that these SVM classifiers achieve very fast, simple, and efficient breast cancer diagnosis. The training results indicated that LSVM has the lowest accuracy of 95.6107 %, while St-SVM performed better than other methods for all performance indices (accuracy = 97.71 %) and is closely followed by LPSVM (accuracy = 97.3282). However, in the validation phase, the overall accuracies of LPSVM achieved 97.1429 %, which was superior to LSVM (95.4286 %), SSVM (96.5714 %), PSVM (96 %), NSVM (96.5714 %), and St-SVM (94.86 %). Value of ROC and MCC for LPSVM achieved 0.9938 and 0.9369, respectively, which outperformed other classifiers. The results strongly suggest that LPSVM can aid in the diagnosis of breast cancer.     

Automatic classifier selection for non-experts

Choosing a suitable classifier for a given dataset is an important part of developing a pattern recognition system. Since a large variety of classification algorithms are proposed in literature, non-experts do not know which method should be used in order to obtain good classification results on their data. Meta-learning tries to address this problem by recommending promising classifiers based on meta-features computed from a given dataset. In this paper, we empirically evaluate five different categories of state-of-the-art meta-features for their suitability in predicting classification accuracies of several widely used classifiers (including Support Vector Machines, Neural Networks, Random Forests, Decision Trees, and Logistic Regression). Based on the evaluation results, we have developed the first open source meta-learning system that is capable of accurately predicting accuracies of target classifiers. The user provides a dataset as input and gets an automatically created high-performance ready-to-use pattern recognition system in a few simple steps. A user study of the system with non-experts showed that the users were able to develop more accurate pattern recognition systems in significantly less development time when using our system as compared to using a state-of-the-art data mining software.     

Enhanced halftone-based secure and improved visual cryptography scheme for colour/binary Images

Visual Cryptography (VC) is gaining attraction during the past few years to secure the visual information in the transmission network. It enables the visual data i.e. handwritten notes, photos, printed text, etc. to encrypt in such a way that their decryption can be done through the human visual framework. Hence, no computational assistance is required for the decryption of the secret images they can be seen through naked eye. In this paper, a novel enhanced halftoning-based VC scheme is proposed that works for both binary and color images. Fake share is generated by the combination of random black and white pixels. The proposed algorithm consists of 3 stages i.e., detection, encryption, and decryption. Halftoning, Encryption, (2, 2) visual cryptography and the novel idea of fake share, make it even more secure and improved. As a result, it facilitates the original restored image to the authentic user, however, the one who enters the wrong password gets the combination of fake share with any real share. Both colored and black images can be processed with minimal capacity using the proposed scheme.