The distribution discrepancy metrics are the core foundation of achieving domain confusion. Therefore, they mainly determine the performance of deep transfer diagnosis models. However, their effectiveness relies on the stability of data local distributions, making them unsuitable for cross-domain machine diagnosis tasks under continuous time-varying conditions. Hence, a new integrated-dispersion manifold distance (IDMD) is proposed to enhance the discrepancy representation capability in dynamic data structures. The maximum entropy-based local distribution (MELD) selection mechanism is designed to represent the global distribution information of time-varying monitoring signals adaptively. Furthermore, the ensemble Grassmann manifold geodesic (EGMG) measurement is constructed to characterize the intrinsic distribution discrepancy information due to complex nonlinear structures of high-dimensional data. The proposed IDMD distribution discrepancy metric is validated against two fault transfer diagnosis experiments under time-varying conditions, including laboratory planetary gearboxes and actual wind turbine bearings. The experimental results demonstrate its effectiveness and advantage over the existing advanced methods.

The rapid advancement of large language models (LLMs) has revolutionized role-playing, enabling the development of general role-playing models. However, current role-playing training has two significant issues: (I) Using a predefined role profile to prompt dialogue training for specific scenarios usually leads to biases and even conflicts between the dialogue and the profile, resulting in training biases. (II) Models learn to imitate the role based solely on the profile, neglecting profile-dialogue alignment at the sentence level. To overcome the aforementioned hurdles, we propose a novel framework Beyond Dialogue, which introduces “beyond dialogue” tasks to align dialogue with profile traits for each scenario, eliminating biases during training. Furthermore, the framework achieves a sentence-level fine-grained alignment between profile and dialogue through an innovative prompting mechanism that generates reasoning data for training. Moreover, the aforementioned methods are fully automated and low-cost. Experimental results demonstrate our model excels in adhering to role profiles, outperforming most proprietary general and specialized role-playing baselines. The code and data are provided in https://github.com/yuyouyu32/BeyondDialogue.

This paper proposes an integrated mixed-integer programming model, termed the Single-Track Railway Cyclic Scheduling Model (SRCSM), for constructing optimized periodic timetables (operating on a recurring 24-hour cycle) for bidirectional single-track railway systems. The SRCSM enhances operational efficiency by precisely considering train arrival/departure times, safety headways, platform track allocations, and meet/pass operations. Validation using real-world data demonstrates its practical applicability, flexibility, and scalability for dynamic timetable optimization, offering a robust tool for improving operational efficiency and safety in single-track railway operations.

Abstract With the widespread application of time series data, the study of classification techniques has become an important topic. Although existing multivariate time series classification (MTSC) methods have made progress, they often rely on one-dimensional (1D) time series, which limits their ability to capture complex temporal dynamics and multiscale features. To address these challenges, a Spectral Convolutional Network (SCNet) is introduced in this work. SCNet effectively transforms 1D time series data into the frequency domain using an enhanced Discrete Fourier Transform (enhanced_DFT), revealing periodicity and key frequency components while reshaping the data into a two-dimensional (2D) time series for better representation. Furthermore, it uses a Spectral Energy Prioritization method to optimize frequency domain energy distribution and a multiscale convolutional module to capture features at different scales, improving the model’s ability to analyze short-term and long-term trends. To validate the effectiveness and superiority, we conducted extensive experiments on 10 sub-datasets from the well-known UEA dataset. The results show that our proposed SCNet achieved the highest average accuracy of 74.3%, which is 2.2% higher than the current state-of-the-art models, demonstrating its potential for practical application and efficiency in MTSC task.

Exploring the intrinsic value of social data has long been a focal point for researchers. Presently, diverse social network data is dispersed across various platforms. While federated learning enables collaborative training with multiple clients, enhancing model performance while safeguarding client-specific information, it often overlooks global user relationships and node-level semantic information, and still faces privacy breaches. Therefore, to address the above shortcomings, this study proposes an efficient privacy preserving federated graph neural network method (PPFedGNN) for social network analysis, thereby achieving dual guarantees of model performance and privacy security. To obtain global user relationships while protecting privacy, we designed a secure coding-based social subgraph aggregation method (SecureSA). This method improves model performance and algorithm efficiency by securely encoding and aggregating the node adjacency relationships across different clients. Additionally, to capture richer global node-level semantic information, we developed a secure social node augmentation method (SecureNA) based on local differential privacy mechanism (LDP). This method enhances model performance while maintaining security by adding noise perturbation to important weights and integrating overlapped node embeddings from different clients. Through experimental verification, it has been found that on Facebook, Blogcatalog, Flickr, and TeleComm datasets, the classification accuracy of PPFedGNN was 0.926, 0.838, 0.662, and 0.901, respectively, outperforming other algorithms. Through ablation experiments, the effectiveness of the global user relationships and node augmentation has been further demonstrated. In addition, we also conducted a theoretical analysis of the security techniques used throughout the training process to demonstrate their safety and efficiency.

The excellent performance of graph neural networks (GNNs), which learn node representations by aggregating their neighborhood information, led to their use in various graph tasks. However, GNNs are black box models, the prediction results of which are difficult to understand directly. Although node attributes are vital for making predictions, previous studies have ignored their importance for explanation. This study presents GAFExplainer, a novel GNN explainer that emphasizes node attributes via attribute augmentation and fusion embedding. The former enhances node attribute encoding for more expressive masks, while the latter preserves the discrimination of node representations across different layers. Together, these modules significantly improve explanation performance. By training the explanatory network, a global view explanation of GNN models is obtained, and reasonably explainable subgraphs are available for new graphs, thus rendering the model well-generalizable. Multiple sets of experimental results on real and synthetic datasets demonstrate that the proposed model provides valid and accurate explanations. In the visual analysis, the explanations obtained by the proposed model are more comprehensible than those in existing work. Further, the fidelity evaluation and efficiency comparison reveal that with an average performance improvement of 8.9$\% $compared with representative baselines, GAFExplainer achieves the best fidelity metrics while maintaining computational efficiency.

In this article, we propose a new graph neural network (GNN) explainability model, CiRLExplainer, which elucidates GNN predictions from a causal attribution perspective. Initially, a causal graph is constructed to analyze the causal relationships between the graph structure and GNN predicted values, identifying node attributes as confounding factors between the two. Subsequently, a backdoor adjustment strategy is employed to circumvent these confounders. Additionally, since the edges within the graph structure are not independent, reinforcement learning is incorporated. Through a sequential selection process, each step evaluates the combined effects of an edge and the previous structure to generate an explanatory subgraph. Specifically, a policy network predicts the probability of each candidate edge being selected and adds a new edge through sampling. The causal effect of this action is quantified as a reward, reflecting the interactivity among edges. By maximizing the policy gradient during training, the reward stream of the edge sequence is optimized. The CiRLExplainer is versatile and can be applied to any GNN model. A series of experiments was conducted, including accuracy (ACC) analysis of the explanation results, visualization of the explanatory subgraph, and ablation studies considering node attributes as confounding factors. The experimental results demonstrate that our model not only outperforms current state-of-the-art explanation techniques, but also provides precise semantic explanations from a causal perspective. Additionally, the experiments validate the rationale for considering node attributes as confounding factors, thereby enhancing the explanatory power and ACC of the model. Notably, across different datasets, our explainer achieved improvements over the best baseline models in the ACC-area under the curve (AUC) metrics by 5.89%, 5.69%, and 4.87%, respectively.

Many transfer learning methods have been proposed to implement fault transfer diagnosis, and their loss functions are usually composed of task-related losses, distribution distance losses, and correlation regularization losses. The intrinsic parameters and trade-off parameters between losses, however, need to be tuned according to the specific diagnosis tasks; thus, the generalization abilities of these methods in multiple tasks are limited. Besides, the alignment goal of most domain adaptation (DA) mechanisms dynamically changes during the training process, which will result in loss oscillation, slow convergence and poor robustness. To overcome the above-mentioned issues, a novel and simple transfer learning diagnosis method named adaptive intermediate class-wise distribution alignment (AICDA) model is proposed, and it is established via the proposed AICDA mechanism, dynamic intermediate alignment (DIA) adaptive layer and AdaSoftmax loss. The AICDA mechanism develops an adaptive intermediate distribution as the alignment goal of multiple source domains and target domains, and it can simultaneously align the global and class-wise distributions of these domains. The DIA layer is designed to adaptively achieve domain confusion without the distribution distance loss and the correlation regularization loss. Meanwhile, to ensure the classification performance of the AICDA mechanism, AdaSoftmax loss is proposed for boosting the separability of Softmax loss. Finally, in order to evaluate the effectiveness and universality of the AICDA diagnosis model to the most degree, various multisource mixed fault transfer diagnosis tasks of wind turbine planetary gearboxes, including DA and domain generalization (DG), are implemented, and the experimental results indicate that our proposed AICDA model has a higher diagnosis accuracy and a stronger generalization ability than other state-of-the-art transfer learning methods.

As Industry 4.0 continues to advance, point cloud registration technology is extensively employed in scenarios such as collaborative defect detection in products and digital twin-assisted assembly. In this paper, we present an end-to-end point cloud registration model, GTPCR, which is based on the utilization of spatial structural information within point cloud data. GTPCR employs a point-wise approach, directly solving rigid rotations based on estimated correspondences without reliance on RANSAC. It conceives of the point cloud as a graph structure in three-dimensional space, encoding nodes across various dimensions. The encoding of an individual node is dictated by its position and centrality. The geometric associations between nodes are contingent on factors such as relative position, Euclidean distance, azimuth, and elevation. Edge feature encoding is dynamically acquired through the learning process from node features. All encodings are incorporated as trainable biases input to the model. In comparison to methods transitioning from local to global, GTPCR demonstrates superior efficiency and heightened scalability. Moreover, due to its adept network architecture design, GTPCR effortlessly expands its applicability to non-rigid registration. Empirical evidence undeniably illustrates GTPCR’s competitive advantage across numerous datasets. In particular, GTPCR shows significant improvements in registration recall of 1.4% and 0.9% over baselines on the 3DMatch and 3DLoMatch datasets, respectively.

In the context of industrial anomaly detection, anomaly point detection is a challenging task due to the rarity and unpredictable nature of anomalous samples. Existing 2D image-based defect detection methods have certain advantages in capturing features such as texture, color, and shape of parts. However, traditional single-modal defect detection methods (such as using only 2D images or only 3D point cloud data) may have limitations in accurately locating abnormal points when faced with complex surface defects on parts. Therefore, a collaborative abnormal localization method (CALM) based on multi-modal images is proposed to improve the accuracy of anomaly localization by fully utilizing information from multiple data sources. First, we propose a synchronized data augmentation method for 2D and 3D images to address the issue of scarce anomalous samples. Then, feature extraction is performed separately on RGB images and 3D point clouds, leveraging the features from both 2D and 3D images and performing multi-modal feature fusion while aligning the features. Finally, anomaly point localization and segmentation are achieved based on the abnormality scores output by the decoder. To validate the effectiveness of our method, experiments are conducted on the MVTec-3D AD dataset. The Pix-AUROC and Pix-AUPRO means of the CALM method reach 0.909 and 0.739, respectively. The experimental results demonstrate that our method achieves high detection accuracy at the pixel level, outperforming some traditional anomaly localization methods.

There have been researches revealing obvious associations between facial phenotypes and non-communicable diseases (NCDs), which enables effective health assessment with the integration of model-based learning methods. However, the paucity and poor quality of available datasets hinder the development of potent algorithms to detect NCDs. To meet this challenge, we propose a method called Exemplar Diffusion Model (EDM), the objective of proposed EDM is to generate facial images that illustrate simulated non-communicable diseases, utilizing a normal facial image as input. Extensive experimental results show that the proposed EDM method outperforms the state-of-the-art methods in terms of Frechet Inception Distance (FID) and Quality Score (QS), with improvements of 0.11 and 0.74, respectively. Furthermore, comprehensive ablation studies and comparative experiments prove the value of proposed EDM method in large-scale facial image dataset generation and non-communicable disease detection.

The performance of neural networks is directly affected by the features obtained from the backbones of fault diagnosis neural networks. To obtain clear features and improve the performance of diagnosis networks, this paper constructs a new block based on a linear transformation. Firstly, the feature vector is divided into a decisive component and an invalid component. Then, it is worth noting that the orthogonality of these two components is beneficial to model learning. According to this, the two components are extracted using two spaces that are constructed based on the relationships between the four fundamental sub-spaces of a matrix. In the four sub-spaces, the row space and the null space are employed to extract the decisive component and useless component, respectively. Both spaces are implemented by two linear layers and are designed as an encoder-decoder structure to ensure the existence of the null space. To ensure the orthogonality of the two spaces, a constraint term is proposed to modify their weights. Lastly, the cosine similarity between the input feature and the invalid component is designed to extract the invalid component entirely. When incorporating the proposed block into some classic classifying neural networks, they can achieve improved diagnosis accuracy. Moreover, when comparing it to two conventional spatial attention mechanisms, the proposed module demonstrates superior overall performance, including diagnosis accuracy, antinoise ability, and generalization ability.

Enhancing weak seismic signals in seismic data processing with a low signal-to-noise ratio (SNR) is a critical task, and it is imperative to attenuate random noise without damaging effective signals. One effective approach to achieving this is through the application of multichannel singular spectrum analysis (MSSA). However, the inherent non-stationary nature of weak signals poses a challenge for MSSA, as it struggles to completely attenuate random noise via the truncating singular value decomposition (TSVD). This study introduces a novel method referred to as adaptive non-stationary signal decomposition (ANSSD) to significantly improve the attenuation ability of seismic random noise of MSSA and enhance weak signals. Recognizing the non-stationary, non-Gaussian, and nonlinear random noise, our proposed method begins by decomposing the prestack data using singular value decomposition (SVD). Subsequently, each column of the left singular vector matrix is subjected to adaptive non-stationary signal decomposition. Finally, the data undergoes processing through truncated singular value decomposition in the frequency domain. For the synthetic data experiment, the SNR of the raw data is -14.03 dB, -4.38 dB after MSSA processing, and 0.29 dB after ANSSD processing. Meanwhile, the filed data processing results also prove that the ANSSD method is superior to the MSSA method in suppressing random noise and enhancing weak signals.

Various federated transfer learning (FTL) methods have been proposed to address domain shift and safeguard data privacy in the field of fault diagnosis. However, the effectiveness of these methods entirely relies on the presumption that the source clients must be homogenous with the target client. Meanwhile, these methods also require that the testing target-domain data are available during the communication process. Considering that target-domain data are typically unseen and heterogenous with source clients, the traditional FTL-based diagnosis methods cannot meet the demand of high data utilization rate and real-time diagnosis in real engineering. To overcome the above-mentioned issues, a novel heterogeneous federated domain generalization network (HFDGN) is proposed to fill the gap in the heterogeneous multisource federated diagnosis. In the HFDGN, the heterogeneous FTL framework is first proposed to achieve the generalized fault diagnosis of a target client by obtaining the common representation mappings from heterogeneous source clients. Additionally, the disentangled domain adaptation (DDA) base model is designed to remove the negative effect caused by noise. This model can enhance the ability of domain confusion and extract the inherent fault-relevant features. The asynchronous unbalanced update paradigm is utilized to optimize the DDA base model. Experimental results on two heterogeneous federated transfer cases prove that HFDGN outperforms other well-known and advanced diagnosis methods. The related code can be downloaded fromhttps://qinyi-team.github.io/2024/05/Heterogeneous-federated-domain-generalization-network.

This study investigated the relations between instructional factors and students’ acceptance and use of flipped learning (FL) in the context of L1 Chinese language learning. A total of 2160 students from ten secondary schools in Hong Kong filled out two questionnaires measuring their perception of the instructional design and implementation of FL in their Chinese language classes and their acceptance of and actual participation in FL activities. Findings of the descriptive analysis indicated that from the perception of students, the design and implementation of FL in Chinese language classes generally adhered to the instructional principles of FL. Students also showed a moderately high level of acceptance of FL and a moderate level of participation in FL activities. The results of structural equation modeling indicated that both the quality of in-class and out-of-class eLearning activities had significant and positive effects on students’ perceived usefulness, ease of use, and enjoyment of FL and, in turn, indirectly affected their actual participation of FL activities. The connection between in-class and out-of-class eLearning activities also had a significant direct effect on student participation. These findings highlight the important role of instructional factors in promoting students’ willingness to accept FL as a new learning approach in a traditional teacher-centered school subject.

Video classification is a challenging task because of the intricate spatiotemporal information present within videos. Current models often rely on 2D or 3D convolutional neural networks. However, convolutional neural networks are difficult to solve the long-range dependency problem. In addition, they are computationally expensive and memory-intensive. To address the challenges, a Multi-layer Transformer is proposed for video classification. The proposed method takes advantage of the high correlation between adjacent frames by grouping them and learning local and global information with a multi-layer structure based on Transformer. First, different frame sampling rates and grouping strategies are tested in the experiments, then comparing the method with state-of-the-art models. The results demonstrate that the proposed method has advanced performance with TOP1 accuracy of 77.8% on the Kinetics-400 dataset and 64.9% on the Something-Something v2 dataset.

Various domain adaptation (DA) methods have been proposed to address distribution discrepancy and knowledge transfer between the source and target domains. However, many DA models focus on matching the marginal distributions of two domains and cannot satisfy fault-diagnosed-task requirements. To enhance the ability of DA, a new DA mechanism, called deep joint distribution alignment (DJDA), is proposed to simultaneously reduce the discrepancy in marginal and conditional distributions between two domains. A new statistical metric that can align the means and covariances of two domains is designed to match the marginal distributions of the source and target domains. To align the class conditional distributions, a Gaussian mixture model is used to obtain the distribution of each category in the target domain. Then, the conditional distributions of the source domain are computed via maximum-likelihood estimation, and information entropy and Wasserstein distance are employed to reduce class conditional distribution discrepancy between the two domains. With joint distribution alignment, DJDA can achieve domain confusion to the highest degree. DJDA is applied to the fault transfer diagnosis of a wind turbine gearbox and cross-bearing with unlabeled target-domain samples. Experimental results verify that DJDA outperforms other typical DA models.

Many domain adaptation (DA) models have been explored for fault transfer diagnosis. However, their successes completely rely on the availability of target-domain samples during the training process. As target domain is usually unseen, the domain-adaptation-based diagnostic models cannot meet the requirement of real-time diagnosis in actual engineering. To achieve the domain confusion in the actual diagnosis scenario, a novel relationship transfer (RT) diagnosis framework is first proposed, which can indirectly measure and reduce the distribution discrepancy between the source domain and unseen target domain. Based on the proposed RT framework, a new domain generalization transfer method, called relationship transfer domain generalization network (RTDGN) is constructed. RTDGN is divided into two phases including task-irrelevant domain adaptation (TIDA) and task-relevant domain generalization (TRDG). In the TIDA phase, a DA adversarial network with several domain discriminators is built to enhance the domain confusion of RT framework. Furthermore, to bring the adversarial network a more general domain confusion ability, a new inverse entropy loss is designed. In the TRDG phase, a residual fusion classifier is constructed to improve the generalization ability of fault classifier. Finally, the experimental results on the wind turbine planetary gearbox dataset and bearing dataset verify the effectiveness and superiority of the proposedRTDGN.

Trajectory data contains rich spatial and temporal information. Turning trajectories into graphs and then analyzing them efficiently in an AI-empowered way is a representative branch of trajectory analysis in IoV and ITS environments, which is of great significance. This research attempts to project trajectories onto road networks to predict traffic conditions. Extracting accurate spatio-temporal dependencies is the key to improving the analysis. However, two problems exist in the current study. The first one is the focus on the network structure while ignoring node features, and the second one is that the structure cannot be fully utilized. In addition, the static spatial structure may not accurately reflect the dynamic real spatial dependency. In response to these problems, a novel Spatio-Temporal Graph Convolutional Networks via View Fusion for Trajectory Data Analytics (STFGCN) model is designed. It contains two independent views: the structural view and feature view. The view fusion layer is further designed. It includes an extended graph convolutional module and a causal dilated module. The extended graph convolutional module fully extracts dynamic spatial dependencies, while the causal dilated module captures time tendencies. Stacked view fusion layers and a view fusion module perform fusion operations based on the advantages of the two views, efficiently integrating information from both. Several experiments are performed on two real-world trajectory datasets. The results show that a better prediction performance is obtained, especially on the long-range time prediction task.

Current computer-aided diagnosis system with deep learning method plays an important role in the field of medical imaging. The collaborative diagnosis of diseases by multiple medical institutions has become a popular trend. However, large scale annotations put heavy burdens on medical experts. Furthermore, the centralized learning system has defects in privacy protection and model generalization. To meet these challenges, we propose two federated active learning methods for multicenter collaborative diagnosis of diseases: the Labeling Efficient Federated Active Learning (LEFAL) and the Training Efficient Federated Active Learning (TEFAL). The proposed LEFAL applies a task-agnostic hybrid sampling strategy considering data uncertainty and diversity simultaneously to improve data efficiency. The proposed TEFAL evaluates the client informativeness with a discriminator to improve client efficiency. On the Hyper-Kvasir dataset for gastrointestinal disease diagnosis, with only 65% of labeled data, the LEFAL achieves 95% performance on the segmentation task with whole labeled data. Moreover, on the CC-CCII dataset for COVID-19 diagnosis, with only 50 iterations, the accuracy and F1-score of TEFAL are 0.90 and 0.95, respectively on the classification task. Extensive experimental results demonstrate that the proposed federated active learning methods outperform state-of-the-art methods on segmentation and classification tasks for multicenter collaborative disease diagnosis.

Domain adaptation has been widely used for knowledge transfer. However, the aligning targets of the existing domain adaptation mechanisms dynamically vary during the training, which leads to the loss oscillation, slow convergence, and poor robustness. To overcome this main problem, a novel domain adaptation mechanism named intermediate distribution alignment (IDA) is proposed. For implementing the end-to-end diagnostic tasks, a feature extractor based on deep convolutional neural network with wide first-layer kernel is first built to fit the posterior distributions of source and target domains. Then through the KL divergence, IDA maps the learned features from the source and target domains into a specific intermediate distribution. It is proved theoretically that IDA can align the prior distributions of two domains. The proposed IDA mechanism is successfully applied to the fault transfer diagnosis of planetary gearboxes without labeled target-domain samples. The comparative results show that the proposed IDA mechanism has higher diagnostic performance than the typical domain adaptation mechanisms.

The growing threat of malware is becoming more and more difficult to ignore. In this paper, a malware feature images generation method is used to combine the static analysis of malicious code with the methods of recurrent neural networks (RNN) and convolutional neural networks (CNN). By using an RNN, our method considers not only the original information of malware but also the ability to associate the original code with timing characteristics; furthermore, the process reduces the dependence on category labels of malware. Then, we use minhash to generate feature images from the fusion of the original codes and the predictive codes from the RNN. Finally, we train a CNN to classify feature images. When we trained very few samples (the proportion of the sample size of training dataset to validation dataset was 1:30), we obtained accuracy over 92 percent. When we adjust the proportion to 3:1, the accuracy exceeds 99.5 percent. As shown in confusion matrices, our method obtains a good result, where the worst false positive rate of all the malware families is 0.0147 and the average false positive rate is 0.0058.

Domain adaption models are widely applied to fault transfer diagnosis. However, the traditional domain adaption models can output only one high-dimensional transfer feature (TF); thus, it is difficult to capture domain-invariant information. Besides, using only one fully connected top classifier probably causes overfitting. Considering these two problems, in this article, we propose a multiscale transfer voting mechanism (MSTVM) to improve the classical domain adaption models and it can be universally applicable to any one of most domain adaption models. MSTVM consists of two substrategies: multiscale transfer mechanism (MSTM) and multiple transfer voting mechanisms (MTVM). The MSTM block includes several branches with multiscale convolutional and pooling operations, and it can output several multiscale TFs to strengthen the domain confusion. The MTVM block consists of multiple top classifiers and a plurality voting operation; thus, MTVM can effectively avoid overfitting and improve generalization ability. MSTVM has the advantages of MSTM and MTVM. Via two transfer diagnosis experiments, the advantage of MSTVM for improving various domain adaption models is verified.

Due to the development of automated malware generation and obfuscation, traditional malware detection methods based on signature matching have limited effectiveness. Thus, a novel approach using visualisation and deep learning technology can play an important role in malware detection and classification. In this study, the authors extract sequences of API calls using dynamic analysis and then use colour mapping rules to create feature images representing malware behaviour. Finally, they train a convolutional neural network to classify different feature images with 9 malware families, and 1000 variants in each family. Experimental results show the effectiveness of the authors’ method. The classification TPR, precision, recall and F1 are all >99%, while the FPR is <0.1%.

K-anonymity and its successors, like l-diversity and t-closeness, are the most popular approaches for privacy preserving data publishing. However, each method has relatively high information loss and computational complexity. In order to solve this problem, this paper presents a fuzzy set based anonymity algorithm, where numerical data are transformed to linguistic data and sensitive data are published in conjunction with fuzzy draft rate. The experimental results show that the fuzzy based algorithm performs better than that of the k-anonymity method from the points of information loss and execution performance. The information loss of the fuzzy based algorithm has been reduced by 40%~50% and the execution time reduced by 48%~59%.

Summary Ensemble learning is one of the main topics of focus in machine learning research. This paper proposes a novel multi‐thread grid‐based multi‐chunk multi‐level ensemble (GMCE) for data stream classification. In order to improve the learning efficiency, GMCE maps different raw data to multiple grids, represents the feature of the grid by the grid first‐order geometric center, and then classifies data based on the grid. Because this grid mapping method compresses the data size significantly, GMCE can increase both the classification accuracy and the computation efficiency. This method has been tested using public KDDCUP99 intrusion detection competition data and five popular P2P applications data. The results show that the GMCE is better than the original multi‐chunk multi‐level ensemble (MCE) in terms of classification accuracy and operation efficiency. For KDDCUP99 data, GMCE has shown more than 98% classification accuracy and 10 times speedup. And for P2P traffic data, GMCE has improved the classification accuracy by 3–4% and achieved greater than four times speedup. Copyright © 2016 John Wiley & Sons, Ltd.

Focusing on the weakness of the traditional network coding, such as latency and high packet loss rate, this paper presents an approach ONTP (Opportunistic Network Coding based on Traffic Prediction), which is based on ARMA (Autoregressive-Moving Average) algorithm. The algorithm is trying to predict the next packet's arriving time, and then use it to determine whether waiting for network coding opportunity. Results show that the algorithm improves the throughput, and effectively reduces network latency, achieves a balance between coding efficiency and user experience.

HBase is a widely used distributed and column-oriented database based on HADOOP and HDFS. But it still has some shortcomings in storing and sharing data. In order to upgrade the security level of HBase, this paper proposes a fine-grained access control framework which extends the access control permissions according to the atomic operations of HBase. Meanwhile, we use XACML for permissions implementation which provides RBAC and Role-Inheritance and guarantees permissions management more conveniently. From the experiment, it shows that the fine grained access control for HBase is practical and the increased overload can almost be ignored while executing the permissions matching.

Intrusion detection, as a dynamic security protection technology, is able to defense the internal and external network attacks. Using Artificial Bee Colony algorithm to optimize the parameters of neural network is to avoid the neural network falling into a local optimum, can solve the problem of slow convergence speed of the neural network algorithm. Also Artificial Bee Colony algorithm can deal with the problem of finding the optimal solutions in a very short period of time. In this paper, An Artificial Bee Colony optimized neural network algorithm is applied to intrusion detection. And the experimental results shows that the optimized method has better detection accuracy and efficiency than the single BP neural network.

As more and more organizations and individuals tend to outsource their data to cloud storage, the security and user privacy protection attract more attention. Previous work mostly focused on the user identity authentication to keep the security, while in this paper, a novel model of cloud secure storage is proposed, which combines the Hadoop distributed file system(HDFS) with symmetric and public-key cryptography. The model uses the HDFS as the storage platform and the XML format as the logical storage structure. This model can not only solve the problem of storing massive data, but also provide data access control mechanisms and ensure sharing data files with confidentiality and integrity among users in cloud environment.

Entropy based intrusion detection which recognizes the network behavior only depends on the packets themselves and do not need any security background knowledge or user interventions, shows great appealing in network security areas. In this paper, we compare two entropy methods, network entropy and normalized relative network entropy (NRNE), to classify different network behaviors. The experimental results show although the two methods are efficient, the improved relative network entropy, NRNE is better which takes more attributes into consideration simultaneously and we can get an overall view of the abnormal network behavior.