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Network-based Intrusion Detection Systems (NIDSs) identify malicious activities by analyzing network traffic. NIDSs are trained with the samples of benign and intrusive network traffic. Training samples belong to either majority or minority classes depending upon the number of available instances. Majority classes consist of abundant samples for the normal traffic as well as for recurrent intrusions. Whereas, minority classes include fewer samples for unknown events or infrequent intrusions. NIDSs trained on such imbalanced data tend to give biased predictions against minority attack classes, causing undetected or misclassified intrusions. Past research works handled this class imbalance problem using data-level approaches that either increase minority class samples or decrease majority class samples in the training data set. Although these data-level balancing approaches indirectly improve the performance of NIDSs, they do not address the underlying issue in NIDSs i.e. they are unable to identify attacks having limited training data only. This paper proposes an algorithm-level approach called Improved Siam-IDS (I-SiamIDS), which is a two-layer ensemble for handling class imbalance problem. I-SiamIDS identifies both majority and minority classes at the algorithm-level without using any data-level balancing techniques. The first layer of I-SiamIDS uses an ensemble of binary eXtreme Gradient Boosting (b-XGBoost), Siamese Neural Network (Siamese-NN) and Deep Neural Network (DNN) for hierarchical filtration of input samples to identify attacks. These attacks are then sent to the second layer of I-SiamIDS for classification into different attack classes using multi-class eXtreme Gradient Boosting classifier (m-XGBoost). As compared to its counterparts, I-SiamIDS showed significant improvement in terms of Accuracy, Recall, Precision, F1-score and values of Area Under the Curve (AUC) for both NSL-KDD and CIDDS-001 datasets. To further strengthen the results, computational cost analysis was also performed to study the acceptability of the proposed I-SiamIDS.

Deep learning has attracted extensive attention in the field of hyperspectral images (HSIs) classification. However, supervised deep learning methods heavily rely on a large amount of label information. To address this problem, in this paper, we propose a two-stage deep domain adaptation method for hyperspectral image classification, which can minimize the data shift between two domains and learn a more discriminative deep embedding space with very few labeled target samples. A deep embedding space is first learned by minimizing the distance between the source domain and the target domain based on Maximum Mean Discrepancy (MMD) criterion. The Spatial–Spectral Siamese Network is then exploited to reduce the data shift and learn a more discriminative deep embedding space by minimizing the distance between samples from different domains but the same class label and maximizes the distance between samples from different domains and class labels based on pairwise loss. For the classification task, the softmax layer is replaced with a linear support vector machine, in which learning minimizes a margin-based loss instead of the cross-entropy loss. The experimental results on two sets of hyperspectral remote sensing images show that the proposed method can outperform several state-of-the-art methods.

Due to its powerful learning capabilities for high-dimensional and complex data, deep semi-supervised clustering algorithms often outperform traditional semi-supervised clustering methods. However, most deep semi-supervised clustering methods cannot fully utilize prior knowledge and unlabeled data. Deep semi-supervised classification algorithms have recently made significant progress in using unlabeled data during training by combining a consistency regularization method. Consistency training encourages network predictions to remain consistent when the input is perturbed. Motivated by the success of consistency regularization methods, we proposed a new semi-supervised clustering framework based on Siamese networks. To leverage the additional structure of unlabeled data and to uncover more information hidden by pairwise constraints, we add a consistency regularization loss, calculated on unlabeled data and pairwise constraints, to our objective function. After consistency training, the connected data can be closer in the learned feature space, while the disconnected data can be far away. To verify the effectiveness of the proposed method, we conducted extensive experiments on several real-world data sets. Experimental results show that the proposed method is more effective than other state-of-the-art methods in clustering performance.

To date, existing Siamese-based trackers have achieved excellent performance. However, in some complex scenarios, using deep convolutional layers alone can not effectively capture powerful representative features. To solve this problem, we propose a Siamese Attention and Deformable features fusion network for visual object Tracking (SiamADT). The proposed SiamADT consists of three modules: a Siamese attention network module for attention feature extraction, a deformable features fusion module, and a classification-regression module for bounding box prediction. Our framework uses ResNet-50 as the backbone for anchor-free tracking. Without tricky anchor hyperparameters tuning and manual intervention, SiamADT is more flexible and versatile. We conduct extensive experiments on four challenging benchmark datasets. The results demonstrate that SiamADT achieves competitive performance among state-of-the-art methods, with real-time speed—30 frames per second.

As demands for understanding visual style among interior scenes increase, estimating style compatibility is becoming challenging. In particular, furniture styles are difficult to define due to their various elements, such as color and shape. As a result, furniture style is an ambiguous concept. To reduce ambiguity, Siamese networks have frequently been used to estimate style compatibility by adding various features that represent the style. However, it is still difficult to accurately represent a furniture’s style, even when using alternate features associated with the images. In this paper, we propose a new Siamese model that can learn from several furniture images simultaneously. Specifically, we propose a one-to-many ratio input method to maintain high performance even when inputs are ambiguous. We also propose a new metric for evaluating Siamese networks. The conventional metric, the area under the ROC curve (AUC), does not reveal the actual distance between styles. Therefore, the proposed metric quantitatively evaluates the distance between styles by using the distance between the embedding of each furniture image. Experiments show that the proposed model improved the AUC from 0.672 to 0.721 and outperformed the conventional Siamese model in terms of the proposed metric.

Flexible manufacturing workshops often encounter scheduling challenges due to complex processes and cumbersome procedures. To address these issues, a dynamic scheduling method is proposed. Initially, a discrete manufacturing workshop scheduling problem model is developed, considering the unique characteristics of the workshop. Digital Twin technology and a Competitive Particle Swarm Optimization algorithm are then integrated to create the scheduling model. Finally, Siamese Neural Networks are incorporated to form a dynamic scheduling mechanism that optimizes disturbance scheduling. The research model demonstrated a quick convergence, efficiently searching for the optimal fitness value using both the Sphere and Griewank functions. In the scheduling objective function test, the model achieved a maximum completion time of 244.8 minutes, the shortest time compared to similar technologies. In Siamese Neural Network experiments, the model successfully suppressed the influence of disturbances, maintaining optimal scheduling performance. Without adjustments for disturbances, the maximum completion time was 58.5 minutes. After optimization, it decreased to 54.2 minutes. These results demonstrate the effective application of the proposed technology in workshop scheduling. The findings provide valuable technical insights for the application of intelligent technologies in workshop scheduling optimization.

Metric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Metric learning methods, which generally use a linear projection, are limited in solving real-world problems demonstrating non-linear characteristics. Kernel approaches are utilized in metric learning to address this problem. In recent years, deep metric learning, which provides a better solution for nonlinear data through activation functions, has attracted researchers’ attention in many different areas. This article aims to reveal the importance of deep metric learning and the problems dealt with in this field in the light of recent studies. As far as the research conducted in this field are concerned, most existing studies that are inspired by Siamese and Triplet networks are commonly used to correlate among samples while using shared weights in deep metric learning. The success of these networks is based on their capacity to understand the similarity relationship among samples. Moreover, sampling strategy, appropriate distance metric, and the structure of the network are the challenging factors for researchers to improve the performance of the network model. This article is considered to be important, as it is the first comprehensive study in which these factors are systematically analyzed and evaluated as a whole and supported by comparing the quantitative results of the methods.

Aquaculture as an industry is quickly expanding. As a result, new aquaculture sites are being established at more exposed locations previously deemed unfit because they are more difficult and resource demanding to safely operate than are traditional sites. To help the industry deal with these challenges, we have developed a decision support system to support decision makers in establishing better plans and make decisions that facilitate operating these sites in an optimal manner. We propose a case-based reasoning system called aquaculture case-based reasoning (AQCBR), which is able to predict the success of an aquaculture operation at a specific site, based on previously applied and recorded cases. In particular, AQCBR is trained to learn a similarity function between recorded operational situations/cases and use the most similar case to provide explanation-by-example information for its predictions. The novelty of AQCBR is that it uses extended Siamese neural networks to learn the similarity between cases. Our extensive experimental evaluation shows that extended Siamese neural networks outperform state-of-the-art methods for similarity learning in this task, demonstrating the effectiveness and the feasibility of our approach.