Search

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]

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]

• A novel and secure block-based image watermarking algorithm is proposed. • The security is enhanced by using visual cryptography. • Considering stability of image edges increases the robustness of the presented method. • The proposed method is completely imperceptible. In this paper a new robust image watermarking algorithm based on blocks classification and visual cryptography (VC) is presented. First the original image is decomposed into non-overlapping blocks. Then, we use canny edge detection and the support vector machine (SVM) classification method to categorize these blocks into smooth and non-smooth classes. The VC technique is used to generate two image shares: A master share that is constructed according to the block classification results and an owner share generated by using the master share together with a binary watermark. To verify the ownership of the image, the watermark can be retrieved by stacking the master share and the owner share. By skipping blocks which are not robust against attacks, the robustness of our proposed watermarking method is significantly improved. Our method is completely imperceptible because the watermark pattern is concealed without modifying the original host image. [ABSTRACT FROM AUTHOR]

In various application domains, high-dimensional multi-label data has become more prevalent, presenting two significant challenges: instances with high-dimensional features and a large number of labels. In the context of multi-label feature selection, the objective is to choose a subset of features from a given set that is highly pertinent for predicting multiple labels or categories associated with each instance. However, certain characteristics of multi-label classification, such as label dependencies and imbalanced label distribution, have often been overlooked although they hold valuable insights for designing effective multi-label feature selection algorithms. In this paper, we propose a feature selection model which exploits explicit global and local label correlations to select discriminative features across multiple labels. In addition, by representing the feature matrix and label matrix in a shared latent space, the model aims to capture the underlying correlations between features and labels. The shared representation can reveal common patterns or relationships that exist across multiple labels and features. An objective function involving L 2 , 1 -norm regularization is formulated, and an alternating optimization-based iterative algorithm is designed to obtain the sparse coefficients for multi-label feature selection. The proposed method was evaluated on 14 real-world multi-label datasets using six evaluation metrics, through comprehensive experiments. The results indicate its effectiveness, surpassing that of several representative methods. [ABSTRACT FROM AUTHOR]

Graph clustering is a fundamental technique in machine learning that has widespread applications in various fields. Deep Nonnegative Matrix Factorization (DNMF) was recently emerged to cope with the extraction of several layers of features, and it has been demonstrated to achieve remarkable results on unsupervised tasks. While DNMF has been applied for analyzing graphs, the effectiveness of the current DNMF approaches for graph clustering is generally unsatisfactory: these methods are intrinsically data representation models, and their objective functions do not capture cluster structures, also ignores direction which is crucial in the directed graph clustering problems. To overcome these downsides, this paper proposes a graph-specific DNMF model based on the Asymmetric NMF which can handle undirected and directed graphs. Inspired by hierarchical graph clustering and graph summarization approaches, the Deep Asymmetric Nonnegative Matrix Factorization (DAsNMF) is introduced for the directed graph clustering problem. In a pseudo-hierarchical clustering setting, DAsNMF decomposes the input graph to extract low-level to high-level node representations and graph representations (summarized graphs). In addition, the asymmetric cosine and PageRank-based similarities are imposed on the proposed model to preserve the local and global graph structures. The learning process is formulated as a unified optimization problem to jointly train representation learning model and clustering model. The extensive experimental studies validate the effectiveness of the proposed method on directed graphs. • A graph specific deep matrix factorization model is proposed for graph clustering. • The direction-aware model can learn hierarchical structures and graph summarization. • Two Asymmetric similarities are utilized to preserve the local and global structures. • A unified optimization approach is provided to solve the proposed objective function. • Experiments demonstrate the effectiveness of the method in directed graph clustering. [ABSTRACT FROM AUTHOR]

Networks are now more crucial than ever for modeling complex systems with interconnected components. Many methods have been developed to infer unobserved links or predict latent links based on observed network topology. However, the effectiveness of current methods is often limited due to the nature of sparseness in large-scale networks. Nonnegative Matrix Factorization (NMF) is one of the most popular low-rank approximation methods which has been successfully applied to large-scale data. However, maintaining generalization from limited observations is still an open challenge. To overcome these challenges, we propose a novel link prediction method based on adversarial NMF, which reconstructs a sparse network by an efficient adversarial training algorithm. Unlike the conventional NMF methods, our model considers potential test adversaries beyond the pre-defined bounds and provides a robust reconstruction with good generalization power. Besides, to preserve the local structure of a network, we use the common neighbor algorithm to extract the node similarity and apply it to low-dimensional latent representation. Simultaneously, we use Frobenius-norm regularization to prevent the factorization from overfitting. We provide an effective Majorization-Minimization method to learn the model parameters. Our method outperforms the state-of-the-art methods, as shown by extensive experiment results on twelve real-world datasets. [ABSTRACT FROM AUTHOR]

• A self-supervised semi-supervised NMF method is proposed for data clustering. • We define an effective semi-supervised NMF model with attractive and repulsive interactions. • An ensemble of semi-supervised NMFs is designed to model a self-supervised one. • The self-supervised model generates pseudo supervisory signals to boost semi-supervised learning. • Experimental results demonstrate the effectiveness of the proposed method in semi-supervised clustering tasks. Semi-supervised nonnegative matrix factorization exploits the strengths of matrix factorization in successfully learning part-based representation and is also able to achieve high learning performance when facing a scarcity of labeled data and a large amount of unlabeled data. Its major challenge lies in how to learn more discriminative representations from limited labeled data. Furthermore, self-supervised learning has been proven very effective at learning representations from unlabeled data in various learning tasks. Recent research works focus on utilizing the capacity of self-supervised learning to enhance semi-supervised learning. In this paper, we design an effective Self-Supervised Semi-Supervised Nonnegative Matrix Factorization (S4NMF) in a semi-supervised clustering setting. The S4NMF directly extracts a consensus result from ensembled NMFs with similarity and dissimilarity regularizations. In an iterative process, this self-supervisory information will be fed back to the proposed model to boost semi-supervised learning and form more distinct clusters. The proposed iterative algorithm is used to solve the given problem, which is defined as an optimization problem with a well-formulated objective function. In addition, the theoretical and empirical analyses investigate the convergence of the proposed optimization algorithm. To demonstrate the effectiveness of the proposed model in semi-supervised clustering, we conduct extensive experiments on standard benchmark datasets. The source code for reproducing our results can be found at https://github.com/ChavoshiNejad/S4NMF. [ABSTRACT FROM AUTHOR]

Link prediction is a fundamental problem in complex network analysis, aimed at predicting missing or forthcoming connections. Recent research investigates the potential of Nonnegative Matrix Factorization (NMF) models in reconstructing sparse networks and using deep NMF to uncover the hierarchical structure. Deep models have demonstrated remarkable performance in various domains, but they are susceptible to overfitting, especially on the limited training data. This paper proposes a novel Link Prediction using Adversarial Deep NMF (LPADNMF) to enhance the generalization of network reconstruction in sparse graphs. The main contribution is the introduction of an adversarial training that incorporates a bounded attack on the input, leveraging the L 2 , 1 norm to generate diverse perturbations. This adversarial training aims to improve the model's robustness and prevent overfitting, particularly in scenarios with limited training data. Additionally, the proposed method incorporates first and second-order affinities as input to capture higher-order dependencies and encourage the extraction of informative features from the network structure. To further mitigate overfitting, a smooth L 2 regularization is applied to the model parameters. To optimize the proposed model effectively, we utilize a majorization-minimization algorithm that efficiently updates the perturbation and latent factors in an iterative manner. Our findings demonstrate that the proposed model not only has the ability to uncover and learn complex structures but also possesses generalization capabilities. This method demonstrates superior performance compared to state-of-the-art methods across eight networks. In comparison to the best-performing approach, LPADNMF exhibited improvements of 2.36% in AUC, 4.67% in Precision, 2.21% in Recall, and 3.73% in F-Measure. [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]

Highlights: [•] Analysis of several texture-based features for predicting mechanical properties. [•] Improving the ANFIS performance by combining texture and space domain features. [•] Proposing the best feature sets for real-time applications. [Copyright &y& Elsevier]

• Some ELMs are used in parallel as experts to map data into discriminative space. • The training data are easily grouped into some clusters in the new feature space. • An index is calculated per group to help finding corresponding groups of a sample. • A tree is used to find corresponding groups of a sample and applying kNN on it. • The method is evaluated on 4 big datasets in absence and presence of noise. kNN algorithm, as an effective data mining technique, is always attended for supervised classification. On the other hand, the previously proposed kNN finding methods cannot be considered as efficient methods for dealing with big data. As there is daily generated and expanded big datasets on different online and offline servers, the efficient methods for such data must be introduced to find kNN. Moreover, massive amounts of data contain more noise and imperfection data samples that significantly increase the need for a robust kNN finding method. In this paper, a new fast and robust kNN finding framework is introduced to deal with the big datasets. In this method, a group of most relevant data samples to an input data sample are detected and the original kNN method is applied on them for finding the final nearest neighbors. The main goal of this method is dealing with the big datasets in an accurate, fast, and robust manner. Here, the training data samples of each label are grouped into some partitions based on the output of some mini-classifiers (i.e. ELM classifier). In fact, the behavior of the mini-classifiers is the basis of partitioning the training data samples. These mini-classifiers are trained using non-overlapping subsets of the training set in the form of each mini-classifier a subset. Here, an index is calculated for each partition to make the corresponding partition finding faster using a tree structure in which each partition index is fallen into a leaf. Then, outputs of the mini-classifiers for an input test sample are used to find the corresponding group of most relevant data samples to the input data sample on the tree. Experimental results indicate that the proposed method has better performance in most cases and comparable performance on other cases of original and noisy big data problems. [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]