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The risk of copyrighted data theft has increased with the growth of digital communication and easy access to digital data. Using watermarking methods has always been an active research subject against unauthorized users to protect copyrighted data. Deep watermarking methods were introduced in recent years to face this challenge, however, the robustness of these methods against graphic attacks and generating high-quality marked-images are still one of the important challenges in this issue. Also, exposure of these methods to the risk of intellectual property infringement is another important challenge in this subject. To address the quality degradation challenge of marked-images, we propose a blind deep image-watermarking method inspired by the Siamese network in which the watermark-codes will be embedded in the network's weights instead of cover-images. Also, a cover-atmosphere is defined for each cover-image which includes the cover-image and its various attacked versions. Favorable robustness is achieved by mapping each cover-atmosphere to its corresponding watermark-code space. Moreover, an independent subspace for non-watermark-images is considered to map into a null-code which makes the method robust against intellectual property infringement attack. The results obtained from the experiment show that the suggested approach outperforms existing methods and can withstand different types of graphical and surrogate model attacks. [ABSTRACT FROM AUTHOR]

Nowadays, social networks sites (SNs) are widely used in a variety of applications such as viral marketing. Given a huge number of users on SNs, the process of selecting appropriate users as the target set is key decision for enterprises to conduct cost-effective targeted marketing and reputation management. Several methods have been presented to find influential users for viral marketing in social networks. One of the weaknesses of previous methods is the selection of target sets who activate similar users and have high spreading ability in the whole network. In this paper, an optimization model is proposed to identify the most influential users of social networks in another view by splitting a big network into small parts as communities and considering both positive and negative interactions between users to create the influence graph. Our model takes the usefulness and similarity of the users into account and tries to select the most profitable users with the lowest possible similarity in which the number of target users is automatically determined. Assessments on real and synthetic networks indicate that the proposed method is capable to select a target set with high profit and gives better performance than other methods. [ABSTRACT FROM AUTHOR]

The "curse of dimensionality" issue caused by high-dimensional datasets not only imposes high memory and computational costs but also deteriorates the capability of learning methods. The main purpose of feature selection is to reduce the dimensionality of these datasets by discarding redundant and irrelevant features, which improves the performance of the learning algorithm. In this paper, a new feature selection algorithm, referred to as FSBACOM, was presented based on the binary ant system (BAS). The proposed method sought to improve feature selection by decreasing redundancy and achieved an optimum solution by increasing search space in a short time. For this purpose, the features were organized sequentially in a circular graph, where each feature was connected to the next one with two select/deselect edges. The proposed representation of the search space reduced computational time significantly, particularly on the high-dimensional datasets. Inspired from genetic algorithm and simulated annealing, a damped mutation strategy was introduced to avoid falling into local optima. In addition, a new idea was utilized to reduce the redundancy between selected features as far as possible. The performance of the proposed algorithm was compared to that of state-of-the-art feature selection algorithms using different classifiers on real-world datasets. The experimental results confirmed that FSBACOM significantly reduces computational time and achieves better performance than other feature selection methods. [ABSTRACT FROM AUTHOR]

The need for efficient image browsing and searching motivates the use of Content-Based Image Retrieval (CBIR) systems. However, they suffer from a big gap between high-level image semantics and low-level features. So, a learning process to reduce the gap seems quite useful. This paper presents a novel Learning Automata (LA)-based approach to improve the CBIR systems. Distributed Learning Automata (DLA) is used in this work to learn the relevant images from textual query feedbacks of the users. Subsequently, the retrieved images are ranked according to the learning outcome and similarity measure. In this study, the similarity between images is evaluated based on two color descriptors: the global color histogram and local color auto-correlogram. A thorough observation and comparison of these color descriptors performances are performed with different color spaces and also with various similarity measures. Experimental results on two publicly available databases demonstrate that the performance of the proposed CBIR system after each round is improved and the system could retrieve images compatible with the users’ perception. [ABSTRACT FROM AUTHOR]

In this paper, we propose a robust block classification based semi-blind video watermarking algorithm using visual cryptography and SURF (Speed-Up Robust Features) features to enhance the robustness, stability, imperceptibility and real-time performance. A method of selecting the best frames in each shot and the best regions or blocks within best frames is proposed to avoid employing frame-by-frame method for generating owner's share in order to enhance robustness as well as reducing time complexity. In our method, Owner's share is generated using the classification of selected robust blocks within the chosen frames along with corresponding watermark information. In extraction process, the SURF features are employed to match the feature points of selected frames with all frames to detect selected frames. Moreover, we resynchronize the embedded regions from distorted video to original sequence using SURF feature points matching. Afterwards, based on these matched feature points, rotation and scaling parameters are estimated next, selected blocks are retrieved using side information being stored eventually, watermark information is reconstructed successfully. Selecting Best frames, best regions, and employing surf features make our method to be highly robust against various kinds of attacks including image processing attacks, geometrical attacks and temporal attacks. Experimental results confirm the superiority of our scheme in case of being applicable in the real world, enhancing robustness and exploiting idea imperceptibility, over previous related methods. [ABSTRACT FROM AUTHOR]

The aim of this research was to predict quality factors of tomato fruit during storage using backscattering and multispectral imaging techniques. To gather the required information for developing prediction models, batches of 200 tomatoes (cv. Pannovy) harvested at two maturity stages, were stored at standard condition up to four weeks. During storage, the modulus of elasticity, moisture content, soluble solid content, titratable acidity, hyperspectral data, and backscattering images were acquired on 40 tomatoes at regular intervals of one week. After extracting the spectral data from 40 points on each sample, they were subjected to preprocessing operations. Several feature selection techniques, including filter (Relief F, Fisher-Score, and t-Score) and wrapper (genetic algorithm) methods were used to find the sensitive wavelengths for each fruit quality parameter. With the novel strategy used, the wavelengths found by the fusion of genetic algorithm and t-Score techniques showed good prediction performance for all considered qualitative parameters. In order to verify the usefulness of selected wavelengths, backscattering and multispectral imaging techniques were applied. The artificial neural network produced the calibration models which gave a reasonably good correlation for estimating the modulus of elasticity, soluble solid content, and titratable acidity at 660 nm and moisture content at 830 nm of tomato from backscattering images. The correlation coefficient between the multispectral and backscattering imaging prediction results and reference measurement results were 0.952 and 0.891 for modulus of elasticity, 0.727 and 0.539 for moisture content, 0.736 and 0.561 for soluble solid content, and 0.811 and 0.706 for titratable acidity, respectively. [ABSTRACT FROM PUBLISHER]