Deep metric learning (DML) has shown promising results in few-shot hyperspectral image (HSI) classification. The core idea of DML is to learn a generalized metric space, in which pixels from unseen classes can be effectively classified with only a few labeled samples. However, the existing DML methods mainly adopt traditional Euclidean distance to achieve the feature metric, which ignores the category uncertainty of spatial-spectral features in mixed and edge pixels. To address this issue, we fully exploit fuzzy logic theory and propose a deep fuzzy metric learning (DFML) method for few-shot HSI classification. First, we design a novel hybrid CNN-transformer spatial-spectral feature extraction network to fully capture the spatial-spectral features of HSI pixels. Then, a fuzzy set representation method based on Gaussian membership function for spatial-spectral features is proposed, which describes the inherent fuzziness of the spatial-spectral features. Finally, to perform the fuzzy similarity measure between the fuzzy sets of query samples and prototypes, we construct a spatial-spectral fuzzy metric space, in which HSI pixels with category uncertainty in their features can be better classified under the condition of small-scale labeled samples. Extensive experimental results on three public HSI datasets demonstrate that the proposed DFML method outperforms the state-of-the-art few-shot HSI classification methods.

Graph Convolutional Networks (GCN) have shown outstanding performance in skeleton-based behavior recognition. However, their opacity hampers further development. Researches on the explainability of deep learning have provided solutions to this issue, with Class Activation Map (CAM) algorithms being a class of explainable methods. However, existing CAM algorithms applies to GCN often independently compute the contribution of individual nodes, overlooking the interactions between nodes in the skeleton. Therefore, we propose a game theory based class activation map for GCN (GT-CAM). First, GT-CAM integrates Shapley values with gradient weights to calculate node importance, producing an activation map that highlights the critical role of nodes in decision-making. It also reveals the cooperative dynamics between nodes or local subgraphs for a more comprehensive explanation. Second, to reduce the computational burden of Shapley values, we propose a method for calculating Shapley values of node coalitions. Lastly, to evaluate the rationality of coalition partitioning, we propose a rationality evaluation method based on bipartite game interaction and cooperative game theory. Additionally, we introduce an efficient calculation method for the coalition rationality coefficient based on the Monte Carlo method. Experimental results demonstrate that GT-CAM outperforms other competitive interpretation methods in visualization and quantitative analysis.

MOTIVATION: Predicting the associations between human microbes and drugs (MDAs) is one critical step in drug development and precision medicine areas. Since discovering these associations through wet experiments is time-consuming and labor-intensive, computational methods have already been an effective way to tackle this problem. Recently, graph contrastive learning (GCL) approaches have shown great advantages in learning the embeddings of nodes from heterogeneous biological graphs (HBGs). However, most GCL-based approaches don't fully capture the rich structure information in HBGs. Besides, fewer MDA prediction methods could screen out the most informative negative samples for effectively training the classifier. Therefore, it still needs to improve the accuracy of MDA predictions. RESULTS: In this study, we propose a novel approach that employs the Structure-enhanced Contrastive learning and Self-paced negative sampling strategy for Microbe-Drug Association predictions (SCSMDA). Firstly, SCSMDA constructs the similarity networks of microbes and drugs, as well as their different meta-path-induced networks. Then SCSMDA employs the representations of microbes and drugs learned from meta-path-induced networks to enhance their embeddings learned from the similarity networks by the contrastive learning strategy. After that, we adopt the self-paced negative sampling strategy to select the most informative negative samples to train the MLP classifier. Lastly, SCSMDA predicts the potential microbe-drug associations with the trained MLP classifier. The embeddings of microbes and drugs learning from the similarity networks are enhanced with the contrastive learning strategy, which could obtain their discriminative representations. Extensive results on three public datasets indicate that SCSMDA significantly outperforms other baseline methods on the MDA prediction task. Case studies for two common drugs could further demonstrate the effectiveness of SCSMDA in finding novel MDA associations. AVAILABILITY: The source code is publicly available on GitHub https://github.com/Yue-Yuu/SCSMDA-master.

Short hairpin RNA (shRNA)-mediated gene silencing is an important technology to achieve RNA interference, in which the design of potent and reliable shRNA molecules plays a crucial role. However, efficient shRNA target selection through biological technology is expensive and time consuming. Hence, it is crucial to develop a more precise and efficient computational method to design potent and reliable shRNA molecules. In this work, we present an interpretable classification model for the shRNA target prediction using the Light Gradient Boosting Machine algorithm called ILGBMSH. Rather than utilizing only the shRNA sequence feature, we extracted 554 biological and deep learning features, which were not considered in previous shRNA prediction research. We evaluated the performance of our model compared with the state-of-the-art shRNA target prediction models. Besides, we investigated the feature explanation from the model's parameters and interpretable method called Shapley Additive Explanations, which provided us with biological insights from the model. We used independent shRNA experiment data from other resources to prove the predictive ability and robustness of our model. Finally, we used our model to design the miR30-shRNA sequences and conducted a gene knockdown experiment. The experimental result was perfectly in correspondence with our expectation with a Pearson's coefficient correlation of 0.985. In summary, the ILGBMSH model can achieve state-of-the-art shRNA prediction performance and give biological insights from the machine learning model parameters.

Due to the complex environment of hyperspectral image (HSI) gathering area, it is difficult to obtain a large number of labeled samples for HSI. Therefore, how to effectively achieve the HSI few-shot classification is a hot spot of current research. Prototypical network (PN) is one of the most classical few-shot learning algorithms, which has been widely employed for few-shot image classification and few-shot object detection. However, existing PN-based algorithms for HSI only utilize the single-scale spatial-spectral feature extracted from the last layer, ignoring the semantic information with different scales contained in the other layers. To solve this problem, a novel multi-scale spatial-spectral prototypical network (MSSPN) is proposed in this letter. The contribution of this letter is threefold. Firstly, a multi-scale spatial-spectral feature extraction algorithm based on ladder structure is proposed to effectively achieve the integration of spatial-spectral features with different scales. Secondly, with the theory of ladder-structure-based extraction algorithm, we design a multi-scale spatial-spectral prototype representation, which is suggested to be more robust and effective in the multi-scale spatial-spectral metric space. Finally, our proposed MSSPN has the advantage of expandability, and can be easily applied for the other PN-based few-shot learning methods. The experimental results on HSI few-shot classification indicate that our proposed MSSPN algorithm can achieve higher accuracy than the representative HSI classifiers and the existing PN-based algorithms.

For the large amount of spatial and spectral information contained in hyperspectral image (HSI), feature description of HSI has attracted widespread concern in recent years. Existing deep learning-based HSI feature description algorithms require a large number of training samples and have poor interpretability. Therefore, it is necessary to develop an efficient HSI features description algorithm with interpretability based on machine learning. Local binary pattern (LBP) is a classical descriptor used to extract the local spatial texture features of images, which has been widely applied to image feature description and matching. However, the existing LBP algorithms for HSI are based on the single-dimensional description, which leads to the limitations on the expression of spatial–spectral information. Therefore, a multidimensional LBP (MDLBP) based on Clifford algebra for HSI is proposed in this article, which is able to extract spatial–spectral feature from multiple dimensions. First, with the theory of the Clifford algebra, a new representation of HSI including spatial and spectral information is built. Second, the geometric relationship between the local geometry of HSI in Clifford algebra space is calculated to realize the local multidimensional description of the local spatial–spectral information. Finally, a novel LBP coding algorithm for HSI is implemented based on the local multidimensional description to calculate the feature descriptor of HSI. The experimental results on HSI classification show that our proposed MDLBP algorithm can achieve higher accuracy than the representative spatial–spectral features and the existing LBP algorithms, especially in the scenery of small-scale training samples.

To detect ship targets in dynamic sea backgrounds, a robust foreground detection method based on background modeling combined with multiple features in the Fourier domain (BMMFF) is proposed. Because the fluctuation of seawater is similar to a sine wave, the amplitude spectrum of the seawater in the Fourier domain has strong energy concentration, and the background model is built in the Fourier domain. The local statistical characteristics can effectively reflect the properties of a specific position, and the target frequency points are extracted by comparing the local statistical characteristic differences between the amplitude spectrum of the test frame and the updated background. Additionally, two different strategies are used to update the background for cases with or without sudden waves in two adjacent test frames. When the target and seawater have different contrast in different scenes, the threshold is set according to the seawater’s fluctuation degree, which is classified into three different levels in the training stage. Moreover, the linear correlation feature between the test frame and the updated background and the oscillation feature of the test frame’s amplitude spectrum are proposed in the Fourier domain, which can better segment targets in relatively calm and violent sea scenes, respectively. Thus, the two features are combined with the background model with a reasonable strategy to further improve the detection accuracy. The experimental results demonstrate that BMMFF outperforms related comparison algorithms in different challenging sea scenes. The average false alarm rate of BMMFF is reduced by approximately 30% in the case where its average detection rate is similar to that of other algorithms.

Hyperspectral remote sensing image (HRSI) can provide additional spectral information of objects and have been widely used in many fields. However, due to the complex environment of the HRSI gathering area, collecting the labeled samples of HRSI is time-consuming and labor-intensive. The scarcity of labeled samples is one of the major difficulties for HRSI analysis and processing. In this letter, a spatial-spectral prototypical network (SSPN) for HRSI is proposed for solving the problem of lack of labeled samples. The contribution of this letter is threefold. First, we design a novel local pattern coding algorithm to combine the spatial and spectral information of HRSI pixels based on spatial neighborhood correlation. Then, a spatial-spectral feature extraction algorithm based on 1-D convolutional neural network (1-D-CNN) is suggested to learn the spatial-spectral metric space where HRSI pixels can be correctly classified with only a few labeled samples. Finally, a novel prototype representation for HRSI in spatial-spectral metric space is proposed to better classify the mixed pixels existing in HRSI. The experimental results on three popular HRSI data sets demonstrate that the proposed SSPN is significantly better than the traditional algorithms.

The estimation of the sea background from maritime infrared video sequences is necessary for target detection. It is challenging to obtain an accurate background model when the scene is a complex and fluctuating sea surface. However, the amplitude spectrum sequences at each frequency point of the pure seawater frames in the Fourier domain are more stable than the gray value sequences of each pixel in the spatial domain. Thus, the background is modeled with a Gaussian distribution in the Fourier domain with the mean and variance adapted over time. In addition, the sea background dynamics are introduced by the variance of the amplitude spectrum sequence, and the dynamic Gaussian discriminant coefficient is set up for each frequency point. Two target discrimination flags are presented based on the background dynamics and Gaussian discriminant process to extract the target more accurately. Furthermore, the entropy filter in the Fourier domain is designed to enhance the target and suppress the sea clutter. The proposed method is successfully tested over several maritime infrared video sequences and has better detection effects compared with several existing state-of-the-art methods.

Spatial–spectral representation plays an important role in hyperspectral images (HSIs) classification. However, many of the existing local feature algorithms for HSIs are based on the two‐dimensional image and do not take full advantage of the information hidden in HSI, such as spatial–spectral locality correlation information, thereby reducing the robustness of these algorithms. In response to these problems, this study presents a robust multi‐view spatial–spectral representation method with the characteristics of HSIs. There are two key techniques in this representation method, called spatial–spectral locality constrained linear coding (SSLLC) and spatial–spectral pyramid matching model (SSPM). Firstly, SSLLC applies the locality information of the feature points and visual words and uses the discriminant information provided by the nearest‐neighbouring spatial–spectral feature points in HSIs. Secondly, SSPM works by partitioning the image into increasingly fine sub‐cubes and uses the cubes to match the local features of the HSIs. The multi‐view representation is tolerant to illumination change, image rotation, affine distortion etc. To assess the validity of authors' algorithm, the authors compared their results with several existing approaches, including a deep learning method. The experimental results show that this representation method can effectively improve the accuracy of HSIs classification.

The first cooperative co-evolutionary algorithm (CCEA) was proposed by Potter and De Jong in 1994 and since then many CCEAs have been proposed and successfully applied to solving various complex optimization problems. In applying CCEAs, the complex optimization problem is decomposed into multiple subproblems, and each subproblem is solved with a separate subpopulation, evolved by an individual evolutionary algorithm (EA). Through cooperative co-evolution of multiple EA subpopulations, a complete problem solution is acquired by assembling the representative members from each subpopulation. The underlying divide-and-conquer and collaboration mechanisms enable CCEAs to tackle complex optimization problems efficiently, and hence CCEAs have been attracting wide attention in the EA community. This paper presents a comprehensive survey of these CCEAs, covering problem decomposition, collaborator selection, individual fitness evaluation, subproblem resource allocation, implementations, benchmark test problems, control parameters, theoretical analyses, and applications. The unsolved challenges and potential directions for their solutions are discussed.

Ciphertext-policy attribute-based encryption (CP-ABE) is a very promising encryption technique for secure data sharing in the context of cloud computing. Data owner is allowed to fully control the access policy associated with his data which to be shared. However, CP-ABE is limited to a potential security risk that is known as key escrow problem, whereby the secret keys of users have to be issued by a trusted key authority. Besides, most of the existing CP-ABE schemes cannot support attribute with arbitrary state. In this paper, we revisit attribute-based data sharing scheme in order to solve the key escrow issue but also improve the expressiveness of attribute, so that the resulting scheme is more friendly to cloud computing applications. We propose an improved two-party key issuing protocol that can guarantee that neither key authority nor cloud service provider can compromise the whole secret key of a user individually. Moreover, we introduce the concept of attribute with weight, being provided to enhance the expression of attribute, which can not only extend the expression from binary to arbitrary state, but also lighten the complexity of access policy. Therefore, both storage cost and encryption complexity for a ciphertext are relieved. The performance analysis and the security proof show that the proposed scheme is able to achieve efficient and secure data sharing in cloud computing.

Ciphertext-policy attribute-based encryption (CP-ABE) has been a preferred encryption technology to solve the challenging problem of secure data sharing in cloud computing. The shared data files generally have the characteristic of multilevel hierarchy, particularly in the area of healthcare and the military. However, the hierarchy structure of shared files has not been explored in CP-ABE. In this paper, an efficient file hierarchy attribute-based encryption scheme is proposed in cloud computing. The layered access structures are integrated into a single access structure, and then, the hierarchical files are encrypted with the integrated access structure. The ciphertext components related to attributes could be shared by the files. Therefore, both ciphertext storage and time cost of encryption are saved. Moreover, the proposed scheme is proved to be secure under the standard assumption. Experimental simulation shows that the proposed scheme is highly efficient in terms of encryption and decryption. With the number of the files increasing, the advantages of our scheme become more and more conspicuous.

Twin support vector machine classifier (TWSVM) was proposed by Jayadeva et al., which was used for binary classification problems. TWSVM not only overcomes the difficulties in handling the problem of exemplar unbalance in binary classification proble

The multilayered feed-forward neural network was applied to automatic target recognition using the high range resolution (HRR) profiles in this paper. To extract effective features from the HRR profiles, the product spectrum originally proposed for the speech signal processing was introduced to the radar target recognition community. The product spectrum was defined as the product of the power spectrum and the group delay function, which could combine the information contained in the magnitude spectrum and phase spectrum of the HRR profiles and carry more details about the shape of the aircrafts. A multilayered feed-forward neural network was selected as classifier. The HRR profiles were obtained using the two-dimensional backscatters distribution data of four different scaled aircraft models. Simulations were presented to evaluate the classification performance with the product spectrum based features. The results demonstrate that the product spectrum based features outperform the original HRR profiles and the multilayered feed-forward neural network is effective for the application of automatic target recognition of aircrafts.

An intrusion detection model AINIDS (an artificial immunological network intrusion detection system) based on the biological immune mechanism is given, which consists of two types of components: detectors and monitor agents. The detectors derive from LISYS (a network-based IDS given by Hofmeyr) and have the same advantages as LISYS has such as: distributability, diversity, error tolerant, dynamic defensive, adaptability, and perfectly integrating the anomaly detection techniques with misuse detection techniques, and so on. Three monitor agents in AINIDS provide the co-stimulation signal to the detectors in order to effectively reduce the false positive alarm. These agents monitor whether the integrity, confidentiality, or availability of a crucial computer system is compromised respectively. Since AINIDS adopts a more objective and reasonable co-stimulation mechanism based on the definition of intrusion and the principle of biological immune than LISYS does, it has very low false positive rate. The preliminary experiment results show the effectiveness of our system.

This paper presents a fast fuzzy c-means (FCM) clustering algorithm with two layers, which is a mergence of hard clustering and fuzzy clustering. The result of hard clustering is used to initialize the c cluster centers in fuzzy clustering, and then the number of iteration steps is reduced. The application of the proposed algorithm to image segmentation based on the two dimensional histogram is provided to show its computational efficience.

The theory of fuzzy sets is applied to enhance X-ray images. The presented algorithm includes a prior enhancement of the contrast in regions using fuzzy operators along with smoothing in a fuzzy property plane which is extracted from the spatial domain using S, pi , and 1- pi functions. The performance of they system is illustrated by application to an image of human skull.>