Fake review detection has the characteristics of huge stream data processing scale, unlimited data increment, dynamic change, and so on. However, the existing fake review detection methods mainly target limited and static review data. In addition, deceptive fake reviews have always been a difficult point in fake review detection due to their hidden and diverse characteristics. To solve the above problems, this article proposes a fake review detection model based on sentiment intensity and PU learning (SIPUL), which can continuously learn the prediction model from the constantly arriving streaming data. First, when the streaming data arrive, the sentiment intensity is introduced to divide the reviews into different subsets (i.e., strong sentiment set and weak sentiment set). Then, the initial positive and negative samples are extracted from the subset using the marking mechanism of selection completely at random (SCAR) and Spy technology. Second, building a semi-supervised positive-unlabeled (PU) learning detector based on the initial sample to detect fake reviews in the data stream iteratively. According to the detection results, the data of initial samples and the PU learning detector are continuously updated. Finally, the old data are continually deleted according to the historical record points, so that the training sample data are within a manageable size and prevent overfitting. Experimental results show that the model can effectively detect fake reviews, especially deceptive reviews.

Sentiment classification can provide the decision support of social applications such as trend judgment, public opinion monitoring, etc. However, the accuracy of sentiment classification for Chinese Weibo is still not satisfactory due to the complexity of Chinese. In addition, affected by the different organisational structure levels, the sentiment tendency of fewer Weibo Comments may be judged to be the opposite. To solve the problem above, this paper presents a Chinese sentiment classification model based on extended sentiment dictionary and organisational structure of comments. First, the sentiment dictionary can be extended by using seven dictionaries, which include the base sentiment dictionary and six additional dictionaries. Then, the sets of rules are constructed, which include inter-sentence rules and organisational structure rules. Finally, comments on three hot topics are crawled and used to make the data sets for sentiment calculation. Accordingly, based on the result of sentiment calculation, sentiment classification is completed. The effectiveness of the proposed model is verified through comparison experiments, and the experimental results are also discussed.

In this paper, adaptive quantized state estimation fusion is deeply studied. To approach the model mismatching problem induced by random quantization, some quantized Kalman filters have been presented in the previous work, such as the quantized Kalman filter with strong tracking filtering (QKF-STF), the variational Bayesian adaptive quantized Kalman filter (VB-AQKF), and a centralized fusion frame-based complex quantized filter called variational Bayesian adaptive QKF-STF (VB-AQKF-STF). Based on the previous work for the single sensor system, a distributed complex quantized filter is designed in this paper. A novel quantized Kalman filter based on multiple-method fusion scheme (QKF-MMF) is proposed. Similar to the VB-AQKF-STF, the QKF-MMF can also realize joint estimation on the state and the quantization error covariance under the distributed fusion frame. Furthermore, it extends the single sensor results to multisensor tracking systems by using centralized and distributed fusion frames. Two multisensor quantized fusion estimators are proposed for a parallel structure with main-secondary processors in the fusion center. The weighted fusion and embedded integration ways are deeply applied to design the multisensor quantized fusion methods. The proposed work can perfect the quantized estimation algorithms and provide different choices for practical engineering applications.

For attribute-based zero-shot learning (ZSL), the attribute classifiers learned previously on the training images may not be usable for the testing images due to that the training and testing images may follow different data distributions. Since domain adaptation learning can effectively perform knowledge transfer under the circumstance of different data distributions, we proposed a novel ZSL method, referred to as multisource domain attribute adaptation based on adaptive multikernel alignment learning (A-MKAL), from the point of view of classifier adaptation. Considering there may be a large difference between object classes, we adopt the clustering method to group the training images according to the class-class correlation measured by the whitened cosine similarity, thus multiple source domains are created. The created multiple source domains are then combined into one weighted source domain to participate in the distribution discrepancy match across domains. In order to adapt the attribute classifier learned on the well-defined source domains to the target domain (the training image set), we designed the A-MKAL by applying the centered kernel alignment to align the attribute kernel matrix and the kernel function of adaptive multiple kernel learning. Experiments on Shoes, OSR, and AWA datasets show that, compared with state-of-the-art methods, our proposed method yields more accurate classification.

Compared with the fixed fusion structure, the flexible fusion structure with mixed fusion methods has better adjustment performance for the complex air task network systems, and it can effectively help the system to achieve the goal under the given constraints. Because of the time-varying situation of the task network system induced by moving nodes and non-cooperative target, and limitations such as communication bandwidth and measurement distance, it is necessary to dynamically adjust the system fusion structure including sensors and fusion methods in a given adjustment period. Aiming at this, this paper studies the design of a flexible fusion algorithm by using an optimization learning technology. The purpose is to dynamically determine the sensors’ numbers and the associated sensors to take part in the centralized and distributed fusion processes, respectively, herein termed sensor subsets selection. Firstly, two system performance indexes are introduced. Especially, the survivability index is presented and defined. Secondly, based on the two indexes and considering other conditions such as communication bandwidth and measurement distance, optimization models for both single target tracking and multi-target tracking are established. Correspondingly, solution steps are given for the two optimization models in detail. Simulation examples are demonstrated to validate the proposed algorithms.

The existing most image processing methods based on physical models can have a significant impact on defogging performance due to inaccurate estimation of the depth of field information. These methods often encounter problems such as low brightness, invisible color distortion, and loss of detail when processing images with poor lighting conditions, such as those taken in coal mines. To address these issues, this paper proposes a new algorithm based on artificial multi-exposure image fusion. The proposed method performs global exposure on images with uneven illumination by combining S-type functions and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm in the Hue-Saturation-Value (HSV) color space. This reduces the spatial dependence of brightness during processing and avoids color distortion problems that may arise in the Red-Green-Blue (RGB) color space. To mitigate the issue of detail loss, a gradient-domain guided filter is used to preserve fine structures in images, while an improved homomorphic filtering algorithm is introduced during the Laplacian pyramid decomposition process to reduce image content loss arising from large dark areas. This paper also conducted subjective, objective, and computational time comparisons to evaluate performance, providing reliable results regarding speed, quality, and reliability in processing hazy images.