ABSTRACT With the development of data mining technology, graph‐based multi‐view clustering methods have been widely studied. However, most of them assume that the sub‐features within the view have the same importance weight, which is not consistent with the actual situation. Therefore, this paper proposes a Multi‐view Clustering method based on Intra‐view Heterogeneity and Inter‐view Compatibility (MC‐IHIC). In this method, the sub‐features in each view are weighted, which not only considers the consistency between views, but also considers the differences between sub‐features in views. In addition, the proposed method obtains the data similarity graph by learning the local manifold structure and partitions the clusters while constructing the similarity graph by imposing the rank constraint, which overcomes the disadvantage that the traditional graph‐based clustering heavily depends on the similarity graph. Finally, the effectiveness of MC‐IHIC is verified by the comparative experiments on three multi‐view datasets.

Phishing emails increasingly embed complex social engineering (SE) tactics to manipulate recipients and increase success rates. However, existing organizational training simulations and detection systems seldom incorporate tactic complexity or reveal how such tactics are linguistically embedded. To address this, we develop methods for generating, annotating, and evaluating SE tactics across three complexity levels in phishing emails. A reliably annotated dataset is constructed via a generate–cross-verify–highlight pipeline, which ensures semantic alignment between labels and embedded SE tactics. These trigger segments are subsequently clustered and synthesized into fine-grained patterns that characterize how each SE tactic manifests at Level 1 (easily), Level 2 (moderately), and Level 3 (deeply). These patterns underpin a multi-level SE framework, validated through LLM-based detection experiments. Detection accuracy declines with increasing tactic complexity, confirming the framework's stratification capability and its utility in training, simulation, and tactic-aware detection design.

Code datasets are invaluable for training neural vulnerability detectors, a promising area within software engineering. Unfortunately, both proprietary and public datasets face the threat of unauthorized exploitation. Moreover, the opaque nature of neural models presents a challenge for external auditing of training datasets, exacerbating the risk of potential misuse. Although watermarking techniques have proven effective in protecting image and natural language datasets, their applicability to code datasets is limited by domain specificity. Current endeavours to preserve the copyrights of code datasets frequently neglect essential control and data dependency information, treating code as a flat structure. To address these gaps, we propose VulCodeMark, a pioneering method that incorporates data and control flow information into code dataset watermarking. VulCodeMark employs two transformations (1) Syntactic Transformation; (2) Semantic Transformation) to generate watermarks, ensuring the preservation of the original program’s functionality while maintaining context-adaptive stealthiness. Experiments have demonstrated that VulCodeMark fulfils essential properties of practical watermarks-including harmlessness, effectiveness, imperceptibility, and robustness. Besides, VulCodeMark additionally supports preliminary probing of model architecture configurations, furnishing valuable forensic evidence in cases of intellectual property infringement.

ABSTRACT Recently, graph semi‐supervised classification for different datasets is faced with some problems, such as low classification accuracy, and error labels in labeled data. To alleviate these problems, we propose a model called anchor‐based adaptive similarity graph learning for semi‐supervised classification (AAGSSL). This model leverages anchors to construct weight matrix associated with anchor and data, and obtains the sparse initial affinity graph by special matrix factorization. It adaptively learns a new similarity graph close to the initial affinity graph, which reduces the model's reliance of classification accuracy on the initial affinity graph. The model enhances the tolerance of error labels in the labeled data and accelerates the process of obtaining predictive labels by adjusting corresponding matrix internal parameters which introduced in model and employing label propagation, respectively. The feasibility and effectiveness of the model were verified in experiments on artificial datasets. Focus on image datasets classification, the comparative experiments on real benchmark image datasets verified the advantages of the proposed model in handling error labels and finding new class. And we additionally evaluate the impact of several parameters on classification performance and choose the best hyperparameters.

ABSTRACT In the field of multiview clustering, how to make full use of information from multiple data sources to improve the clustering performance has become a hot research topic. However, the rapid growth of high‐dimensional multiview data brings great challenges to the research of multiview clustering algorithms, especially the time and space complexity of the algorithms. As an effective solution, anchor‐based technique has gained wide attention in large‐scale multiview clustering tasks. Nevertheless, the current anchor‐based methods fail to fully take into account the importance of different views and the difference and diversity of anchors at the same time, which limits the clustering performance to some extent. To address these problems, we propose a dual‐weighted multiview clustering based on anchor (DwMVCA). First, we effectively distinguish the different impacts of high‐quality and low‐quality views on clustering by adaptively learning the weights of different views. Second, by introducing the adaptive weighting matrix of anchors and self‐correlation matrix regularization term, the difference and diversity of anchors are fully considered to effectively reduce the effect of redundant information on clustering. Furthermore, we design a three‐step alternating optimization algorithm to solve the resultant optimization problem and prove its convergence. Extensive experimental results show that the proposed DwMVCA has obvious advantages in clustering performance on large‐scale datasets, especially on datasets with more than 100,000 samples that still maintain linear time complexity.

In Internet of Things (IoT) networks, the IoT sensors collect valuable raw data required to sustain Artificial Intelligence (AI) based networks operation. AI models are data-driven as they use the data to make accurate network security, management, and operational decisions. Unfortunately, the sensors are deployed in harsh environments which affects the sensor behaviour and eventually the networks' operations. Further, IoT devices are typically vulnerable to a range of malicious events. Therefore, IoT sensor's correct operation including resilience to failure is essential for sustained operations. Naturally, the state variables of time-series data can be changed, i.e., the data streams generated in these situations can be incorrect, incomplete or missing, and sparse presenting a significant challenge for real-time decision-making ability of AI models to make explainable and intelligent management and control decisions. In this paper, we aim to alleviate this fundamental problem to predict the missing and faulty reading correctly so that the decision-making ability of the AI models should not deteriorate in the presence of incorrect, missing, and highly imbalanced data sets. We use a novel approach using fuzzy-based information decomposition to recover the missed data values. We use three data sets, and our preliminary results show that our approach effectively recovers the missed or compromised data samples and help AI models in making accurate decision. Finally, the limitations and future work of this research have been discussed.

Smart homes (SHs) are rapidly evolving to incorporate intelligent features, including environment management, home automation, and human–machine interactions. However, safety and security risks of SHs hinder their wide adoption. Many work attempts to provide defense mechanisms to ensure safety and security against interrule vulnerabilities and spoofing attacks. This article proposes IoTFuzz, a fuzzing framework that dynamically address cyber security and physical safety aspects of SHs through targeted policies. IoTFuzz mutates the inputs from policies, human activities, indoor environment, and real-life outdoor weather conditions. In addition to the binary status of devices, the continuous-value status in SHs is leveraged to perform mutation and simulation. The policies are expressed as temporal logic formulas with time constraints. For large-scale testing, IoTFuzz employs digital twins to simulate normal behaviors, outdoor environment impacts, and human activities in SHs. Moreover, IoTFuzz can also intelligently infer rule-policy correlation based on natural language processing (NLP) techniques. The evaluation of IoTFuzz in a configured SH with 15 rules and 10 predefined unique policies demonstrates its effectiveness in revealing the impacts of real-life outdoor environment. The experimental results demonstrate a range of violations, with a maximum of 4154 violations and a minimum of 41 violations observed over an 8-year period under varying weather conditions. IoTFuzz also identifies the potential risks associated with improper human activities, accounting for up to 35.4% of risky situations in SHs.

Remote-sensing images serve as key data sources which play a crucial role in recording the target information of ground features. Due to the limitations of the existing imaging equipment, environments, and transmission conditions, the obtained remote-sensing images are usually contaminated by noise in real-world scenarios. To address this problem, we propose a dual-complementary convolution network (DCCNet), including structural and detailed subnetwork, for repairing the structure and details of noisy remote-sensing images. More specifically, they generate multiresolution inputs via discrete wavelet transform and shuffling operation, respectively. Since the convolution operation is imposed on low-resolution inputs, the network parameters are considerably reduced. Experimental evaluations demonstrate that our proposed network exhibits superior performance to other competing methods in remote-sensing public datasets. The code of the DCCNet is available athttps://github.com/20155104009/DCCNet.

A major cause of security incidents such as cyber attacks is rooted in software vulnerabilities. These vulnerabilities should ideally be found and fixed before the code gets deployed. Machine learning-based approaches achieve state-of-the-art performance in capturing vulnerabilities. These methods are predominantly supervised. Their prediction models are trained on a set of ground truth data where the training data and test data are assumed to be drawn from the same probability distribution. However, in practice, the test data often differs from the training data in terms of distribution because they are from different projects or they differ in the types of vulnerability. In this article, we present a new system forCrossDomain SoftwareVulnerabilityDiscovery (CD-VulD) using deep learning (DL) and domain adaptation (DA). We employ DL because it has the capacity of automatically constructing high-level abstract feature representations of programs, which are likely of more cross-domain useful than the handcrafted features driven by domain knowledge. The divergence between distributions is reduced by learning cross-domain representations. First, given software program representations, CD-VulD converts them into token sequences and learns the token embeddings for generalization across tokens. Next, CD-VulD employs a deep feature model to build abstract high-level presentations based on those sequences. Then, the metric transfer learning framework (MTLF) technique is employed to learn cross-domain representations by minimizing the distribution divergence between the source domain and the target domain. Finally, the cross-domain representations are used to build a classifier for vulnerability detection. Experimental results show that CD-VulD outperforms the state-of-the-art vulnerability detection approaches by a wide margin. We make the new datasets publicly available so that our work is replicable and can be further improved.

Abstract Many very deep neural networks are proposed to obtain accurate super‐resolution reconstruction of remote sensing images. However, the deeper the network for image SR is, the more difficult it is to train. The low‐resolution inputs and features contain abundant low‐frequency information and noise, which are treated equally as the high‐frequency information to across the network. To solve these problems, a novel single‐image super‐resolution algorithm named pre‐training of gated convolution neural network (PGCNN) is proposed for remote sensing images. The proposed PGCNN consists of several residual blocks with long skip connections. Each residual block contains an additional well‐designed gated convolution unit, which provides different weights to high‐frequency information and low‐frequency information to control the transmission of information, making the main network focus on learning high‐frequency information. Compared with several state‐of‐the‐art methods, experimental results on the remote sensing datasets (SIRI‐WHU, NWPU‐RESISC45, RSSCN7 and UC‐Merced‐Land‐Use) show that the proposed PGCNN has the accuracy and visual improvements.

Electron microscopy (EM) image segmentation plays an important role in computer‐aided diagnosis of specific pathogens or disease. However, EM image segmentation is a laborious task and needs to impose experts knowledge, which can take up valuable time from research. Convolutional neural network (CNN)‐based methods have been proposed for EM image segmentation and achieved considerable progress. Among those CNN‐based methods, UNet is regarded as the state‐of‐the‐art method. However, the UNet usually has millions of parameters to increase training difficulty and is limited by the issue of vanishing gradients. To address those problems, the authors present a novel highly parameter efficient method called DenseUNet, which is inspired by the approach that takes particular advantage of recent advances in both UNet and DenseNet. In addition, they successfully apply the weighted loss, which enables us to boost the performance of segmentation. They conduct several comparative experiments on the ISBI 2012 EM dataset. The experimental results show that their method can achieve state‐of‐the‐art results on EM image segmentation without any further post‐processing module or pre‐training. Moreover, due to smart design of the model, their approach has much less parameters than currently published encoder–decoder architecture variants for this dataset.

The traditional Frost filter is improved by the adaptive windowing and adaptive tuning factor for the synthetic aperture radar (SAR) images in this letter. The proposed double-adaptive Frost filter simultaneously makes the window size and the tuning factor adaptively adjusted in terms of the regional characteristics, leading to an effective balance between speckle suppression and edge preservation. The despeckling experiments on the simulated and real SAR images demonstrate that in comparison to the Lee filter, the Gamma maximum a posteriori (MAP) filter, the traditional Frost filter, and the Frost filter only with the adaptive tuning factor, the double-adaptive Frost filter sufficiently suppresses the speckle in homogeneous regions and in edge regions and effectively preserves the edges and fine details.

Software vulnerability has long been an important but critical research issue in cybersecurity. Recently, the machine learning (ML)-based approach has attracted increasing interest in the research of software vulnerability detection. However, the detection performance of existing ML-based methods require further improvement. There are two challenges: one is code representation for ML and the other is class imbalance between vulnerable code and nonvulnerable code. To overcome these challenges, this article develops a DeepBalance system, which combines the new ideas of deep code representation learning and fuzzy-based class rebalancing. We design a deep neural network with bidirectional long short-term memory to learn invariant and discriminative code representations from labeled vulnerable and nonvulnerable code. Then, a new fuzzy oversampling method is employed to rebalance the training data by generating synthetic samples for the class of vulnerable code. To evaluate the performance of the new system, we carry out a series of experiments in a real-world ground-truth dataset that consists of the code from the projects of LibTIFF, LibPNG, and FFmpeg. The results show that the proposed new system can significantly improve the vulnerability detection performance. For example, the improvement is 15% in terms of F-measure.

Internet of Things (IoT) integrates a variety of software (e.g., autonomous vehicles and military systems) in order to enable the advanced and intelligent services. These software increase the potential of cyber-attacks because an adversary can launch an attack using system vulnerabilities. Existing software vulnerability analysis methods used to be relying on human experts crafted features, which usually miss many vulnerabilities. It is important to develop an automatic vulnerability analysis system to improve the countermeasures. However, source code is not always available (e.g., most IoT related industry software are closed source). Therefore, vulnerability detection on binary code is a demanding task. This article addresses the automatic binary-level software vulnerability detection problem by proposing a deep learning-based approach. The proposed approach consists of two phases: binary function extraction, and model building. First, we extract binary functions from the cleaned binary instructions obtained by using IDA Pro. Then, we employ the attention mechanism on top of a bidirectional long short-term memory for building the predictive model. To show the effectiveness of the proposed approach, we have collected datasets from several different sources. We have compared our proposed approach with a series of baselines including source code-based techniques and binary code-based techniques. We have also applied the proposed approach to real-world IoT related software such as VLC media player and LibTIFF project that used on Autonomous Vehicles. Experimental results show that our proposed approach betters the baselines and is able to detect more vulnerabilities.

Summary Software vulnerability is a critical issue in the realm of cyber security. In terms of techniques, machine learning (ML) has been successfully used in many real‐world problems such as software vulnerability detection, malware detection and function recognition, for high‐quality feature representation learning. In this paper, we propose a performance evaluation study on ML based solutions for software vulnerability detection, conducting three experiments: machine learning‐based techniques for software vulnerability detection based on the scenario of single type of vulnerability and multiple types of vulnerabilities per dataset; machine learning‐based techniques for cross‐project software vulnerability detection; and software vulnerability detection when facing the class imbalance problem with varying imbalance ratios. Experimental results show that it is possible to employ software vulnerability detection based on ML techniques. However, ML‐based techniques suffer poor performance on both cross‐project and class imbalance problem in software vulnerability detection.

Due to the environment and equipment are not controllable, the process of face image acquisition is inevitable to be interfered by external factors, and there are usually only a small number of available face images. Insufficient samples are not conducive to face recognition. Therefore, it is a popular scheme to produce virtual samples based on the available training samples. In this study, the authors first take the symmetry of human face into account, and propose a novel method to generate virtual samples. Then a representation‐based classification method and the score fusion strategy are applied to both original face images and virtual images to perform face recognition. Several sparse representation‐based classification algorithms are compared on ORL, FERET and GT databases. Experimental results show that the authors’ method is effective for improving the face recognition.

Micro-Doppler (MD) radar signatures characterize rich motion information of the targets and are of great significance in target recognition. In this letter, we propose a novel high-order particle filter track-before-detect (PF-TBD) approach for the MD curves extraction. In the proposed approach, the sinusoidal Doppler frequency curve is treated as the state, whose dynamic model is described as a high-order Markov chain. First, the state equation is divided into two parts, the translational motion part represented as a first-order dynamic process and the micromotion part represented as a high-order dynamic process including static model parameters. Then, a kernel smoothing approach is introduced for the static model parameters estimation, and the auxiliary particle filter (APF) is utilized for the instantaneous Doppler curves extraction. Finally, the experiments on the electromagnetic analysis data are carried out to validate the performance of the proposed method.

Most of the approaches used for Landslide inventory mapping (LIM) rely on traditional feature extraction and unsupervised classification algorithms. However, it is difficult to use these approaches to detect landslide areas because of the complexity and spatial uncertainty of landslides. In this letter, we propose a novel approach based on a fully convolutional network within pyramid pooling (FCN-PP) for LIM. The proposed approach has three advantages. First, this approach is automatic and insensitive to noise because multivariate morphological reconstruction is used for image preprocessing. Second, it is able to take into account features from multiple convolutional layers and explore efficiently the context of images, which leads to a good tradeoff between wider receptive field and the use of context. Finally, the selected PP module addresses the drawback of global pooling employed by convolutional neural network, FCN, and U-Net, and, thus, provides better feature maps for landslide areas. Experimental results show that the proposed FCN-PP is effective for LIM, and it outperforms the state-of-the-art approaches in terms of five metrics, $Precision$ , $Recall$ , $Overall~Error$ , $F$ -$score$ , and $Accuracy$ .

Grouped sparse representation classification methods (GSRCMs) have been attracted much attention by scholars, especially in face recognition. However, pervious literatures of GSRCMs only fuse the scores from different groups to classification the test sample, not consider relationships of the groups. Moreover, in real-world application, many methods of face recognition cannot obtain satisfied recognition accuracies because of the variation of poses, illuminations and facial representations of face image. In order to overcome above-mentioned bottlenecks, in this paper, we proposed a novel grouped fusion-based method in face recognition. The proposed method uses the axis-symmetrical property of face to designs a framework and perform it on original training set to generate a kind of virtual samples. The virtual samples are able to reflect the possible change of face images. Meanwhile, to consider the relationship of different groups and strengthen the representation capability of test sample, the proposed method exploits a novel weighted fusion approach to classify the test sample. Experimental results on five face databases demonstrate that our method is reasonable and can obtain higher recognition rate than the other 11 state-of-the-art methods.

A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely used for grayscale and color image segmentation. However, most of them are time-consuming and unable to provide desired segmentation results for color images due to two reasons. The first one is that the incorporation of local spatial information often causes a high computational complexity due to the repeated distance computation between clustering centers and pixels within a local neighboring window. The other one is that a regular neighboring window usually breaks up the real local spatial structure of images and thus leads to a poor segmentation. In this work, we propose a superpixel-based fast FCM clustering algorithm that is significantly faster and more robust than stateof-the-art clustering algorithms for color image segmentation. To obtain better local spatial neighborhoods, we first define a multiscale morphological gradient reconstruction operation to obtain a superpixel image with accurate contour. In contrast to traditional neighboring window of fixed size and shape, the superpixel image provides better adaptive and irregular local spatial neighborhoods that are helpful for improving color image segmentation. Second, based on the obtained superpixel image, the original color image is simplified efficiently and its histogram is computed easily by counting the number of pixels in each region of the superpixel image. Finally, we implement FCM with histogram parameter on the superpixel image to obtain the final segmentation result. Experiments performed on synthetic images and real images demonstrate that the proposed algorithm provides better segmentation results and takes less time than state-of-the-art clustering algorithms for color image segmentation.

Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed, as it is able to filter out seeds (regional minima) to reduce over-segmentation. However, the MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. First, AMR can adaptively filter out useless seeds while preserving meaningful ones. Second, AMR is insensitive to the scale of structuring elements because multiscale structuring elements are employed. Finally, the AMR has two attractive properties: monotonic increasingness and convergence that help seeded segmentation algorithms to achieve a hierarchical segmentation. Experiments clearly demonstrate that the AMR is useful for improving performance of algorithms of seeded image segmentation and seed-based spectral segmentation. Compared to several state-of-the-art algorithms, the proposed algorithms provide better segmentation results requiring less computing time.

Machine learning (ML) is commonly used in multiple disciplines and real-world applications, such as information retrieval, financial systems, health, biometrics and online social networks. However, their security profiles against deliberate attacks have not often been considered. Sophisticated adversaries can exploit specific vulnerabilities exposed by classical ML algorithms to deceive intelligent systems. It is emerging to perform a thorough security evaluation as well as potential attacks against the machine learning techniques before developing novel methods to guarantee that machine learning can be securely applied in adversarial setting. In this paper, an effective attack strategy for crafting foreign support vectors in order to attack a classic ML algorithm, the Support Vector Machine (SVM) has been proposed with mathematical proof. The new attack can minimize the margin around the decision boundary and maximize the hinge loss simultaneously. We evaluate the new attack in different real-world applications including social spam detection, Internet traffic classification and image recognition. Experimental results highlight that the security of classifiers can be worsened by poisoning a small group of support vectors.

It is a challenge to recognize holoscopic 3D (H3D) micro-gesture based on general vision techniques because images captured by H3D imaging system are unclear, i.e., the captured images include a large number of blurred grids. Many feature extraction methods can not be directly used for H3D images because the edge information of the grids will be captured. In this paper, we propose a fast and robust preprocessing method for H3D image reconstruction. The reconstructed images are clear and can be used directly for feature extraction or feature learning. Two contributions are presented in this paper. Firstly, we propose a bi-directional morphological filter used for enhancing the grids in an H3D image. Secondly, we propose a fast clustering algorithm with spatial information to extract grids from the H3D image. Because bi-directional morphological filter is able to incorporate local spatial information to the objective function of the fast clustering algorithm, the grids in H3D images are removed completely. Moreover, because the fast clustering algorithm perform clustering on gray levels of H3D images, a small computational cost is required. The proposed method is used to reconstruct H3D images to obtain multiple images with low resolution captured for 3D gesture recognition. Experiments show that the proposed preprocessing method is not only able to obtain better images that are clear and suitable for feature extraction or feature learning, but also is able to improve recognition accuracy in the micro-gesture recognition based on H3D imaging systems.

Summary Recently, online social network (OSN) such as Twitter has become an important and popular source for real‐time information and news dissemination, and Twitter is inevitably a prime target of spammers. It has been showed that the security threats caused by Twitter spam can reach far beyond the social media platform itself. To mitigate the damage caused by Twitter spam, machine learning classification algorithms have been employed by researchers and communities to detect the Twitter spam. However, most of these studies have overlooked the class imbalance problem in Twitter spam detection. In this paper, we have studied the class imbalance problem in Twitter spam detection. Firstly, we have conducted a comparative study regarding some popular methods in handling the class imbalance problem in order to identify the most effective approach for addressing the class imbalance problem. Then, we have conducted another comparative study from Twitter spam detection based on several classic techniques. Experimental results demonstrate that a fuzy‐based ensemble learning can significantly improve the classification performance on imbalance ground truth Twitter data.

Summary Twitter spam has long been a critical but difficult problem to be addressed. So far, researchers have developed a series of machine learning–based methods and blacklisting techniques to detect spamming activities on Twitter. According to our investigation, current methods and techniques have achieved the accuracy of around 87%. However, because of the problems of spam drift and information fabrication, these machine learning–based methods cannot efficiently detect spam activities in real‐life scenarios. Meanwhile, the blacklisting method also cannot catch up with the variations of spamming activities, as manually inspecting suspicious URLs is extremely timeconsuming. In this paper, we proposed a novel technique based on deep‐learning technique to address the above challenges. The syntax of each tweet will be learned through WordVector and trained by deep learning. We then constructed a binary classifier to differentiate spam and regular tweets. In experiments, we collected and labeled a 10‐day real tweet dataset as ground truth to evaluate our proposed method. We first went for empirical analysis with a series of comparisons to other methods: (1) performance of different classifiers, (2) other existing text‐based methods, and (3) nontext‐based detection techniques. According to the experiment results, our proposed method largely outperformed previous methods. We further conducted principle component analysis on typical methods to theoretically justify the outperformance of our method. We extracted all kinds of features via dimensionality reduction. It was found that our features were most distinct among all the detection methods. This well demonstrated the outperformance of our method.

Among all image representation and classification methods, sparse representation has proven to be an extremely powerful tool. However, a limited number of training samples are an unavoidable problem for sparse representation methods. Many efforts have been devoted to improve the performance of sparse representation methods. In this study, the authors proposed a novel framework to improve the classification accuracy of sparse representation methods. They first introduced the concept of the approximations of all training samples (i.e., virtual training samples). The advantage of this is that the application of virtual training samples can allow noise in original training samples to be partially reduced. Then they proposed an efficient and competent objective function to disclose more discriminant information between different classes, which is very significant for obtaining a better classification result. The devised sparse representation method employs both the original and virtual training samples to improve the classification accuracy since the two kinds of training samples makes sample information to be fully exploited in a good way, also satisfactory robustness to be obtained. The experimental results on the JAFFE, ORL, Columbia Object Image Library (COIL‐100) AR and CMU PIE databases show that the proposed method outperforms the state‐of‐art image classification methods.

Class imbalance and missing values are two critical problems in pattern classification. Researchers have proposed a number of techniques to address each of the problems. However, no single technique can solve the two problems. Moreover, the simple combination approach cannot accurately classify the imbalanced data with missing values. This paper develops a fuzzy-based information decomposition (FID) method to simultaneously address these two problems. In the new FID method, the two different problems are treated as the same missing data estimation problem. In particular, FID rebalances the training data by creating synthetic samples for the minority class. The proposed scheme has two steps: weighting and recovery. In the weighting step, the weights produced by the fuzzy membership functions are used to quantify the contribution of the observed data to the missing estimation. In the recovery step, missing values will be estimated by taking into account different contribution of the observed data. To evaluate the performance of the new FID method, a large number of classification experiments have been carried out on 27 well-known datasets. The results show that the FID method significantly outperforms other ten state-of-the-art individual methods and eight combination methods when missing values and imbalanced data present at the same time.

Spam has become a critical problem in online social networks. This paper focuses on Twitter spam detection. Recent research works focus on applying machine learning techniques for Twitter spam detection, which make use of the statistical features of tweets. We observe existing machine learning based detection methods suffer from the problem of Twitter spam drift, i.e., the statistical properties of spam tweets vary over time. To avoid this problem, an effective solution is to train one twitter spam classifier every day. However, it faces a challenge of the small number of imbalanced training data because labelling spam samples is time-consuming. This paper proposes a new method to address this challenge. The new method employs two new techniques, fuzzy-based redistribution and asymmetric sampling. We develop a fuzzy-based information decomposition technique to re-distribute the spam class and generate more spam samples. Moreover, an asymmetric sampling technique is proposed to re-balance the sizes of spam samples and non-spam samples in the training data. Finally, we apply the ensemble technique to combine the spam classifiers over two different training sets. A number of experiments are performed on a real-world 10-day ground-truth dataset to evaluate the new method. Experiments results show that the new method can significantly improve the detection performance for drifting Twitter spam.

In this paper, we propose a level-set based segmentation method for medical images with intensity inhomogeneity. Maximum a Posteriori estimation is adopted to combine image segmentation and bias field correction into a unified framework. Within this framework, both contour prior and bias field prior can be fully used. In order to restrict bias field, we introduce an adaptive regularization. Based on this new adaptive regularization, the bias field is estimated more smooth and the input medical image with intensity inhomogeneity is recovered more clearly. Especially, the estimated bias field of our method introduces less structure information obtained from input image. Experimental results on both synthetic and real images show the advantages of our method in both segmentation and bias field correction accuracies as compared with the state-of-the-art approaches.

SUMMARY In this paper, a new local region‐based active contour model that is driven by normalized local image fitting energy is proposed. By considering the local image fitting energy to extract the local image information, our model can effectively and efficiently segment images with intensity inhomogeneities. In addition, to keep the smoothness of the level set function, the time‐consuming reinitialization step widely adopted in traditional level set methods can be avoided by introducing a Gaussian filtering process. Experimental results on synthetic and real images demonstrate that the proposed model has much less CPU time and sensitivity to the initial contour than the well‐known local binary fitting model. Copyright © 2013 John Wiley & Sons, Ltd.