- Chao Tan;Genlin Ji;Richen Liu;Yanqiu Cao;
According to smoothness assumption, local topological structure can be shared between feature and label manifolds. This study proposes a new algorithm based on Local Tangent Space Alignment(LTSA) to implement the label enhancement process. In general, we first establish a learning model for feature extraction in label space and use a feature extraction method of LTSA to guide the reconstruction of label manifolds. Then, we establish an unconstrained optimization model based on the optimal theory presented in this paper. The model is suitable for solving problems with a large number of sample points. Finally, the experiment results show that the algorithm can effectively improve the training speed and multilabel dataset prediction accuracy.
2021年02期 v.26 135-145页 [查看摘要][在线阅读][下载 531K] [下载次数：32 ] |[网刊下载次数：0 ] |[引用频次：5 ] |[阅读次数：0 ] - Guo Pu;Lijuan Wang;Jun Shen;Fang Dong;
In recent years, machine learning-based cyber intrusion detection methods have gained increasing popularity. The number and complexity of new attacks continue to rise; therefore, effective and intelligent solutions are necessary. Unsupervised machine learning techniques are particularly appealing to intrusion detection systems since they can detect known and unknown types of attacks as well as zero-day attacks. In the current paper,we present an unsupervised anomaly detection method, which combines Sub-Space Clustering(SSC) and One Class Support Vector Machine(OCSVM) to detect attacks without any prior knowledge. The proposed approach is evaluated using the well-known NSL-KDD dataset. The experimental results demonstrate that our method performs better than some of the existing techniques.
2021年02期 v.26 146-153页 [查看摘要][在线阅读][下载 2214K] [下载次数：51 ] |[网刊下载次数：0 ] |[引用频次：43 ] |[阅读次数：0 ] - Jing Sun;Xiaoping Jiang;Jin Liu;Fan Zhang;Congying Li;
Following the popularity of digital video application, video copying and dissemination have become very easy; however, this makes video hacking and piracy a potential threat in video communication. Video watermarking technology can solve the problem of copyright protection, and thus, it has been extensively researched. The robustness of the video watermarking algorithm in the bitstream domain is poor, especially the anti-recompression ability, since the watermarked video may be compressed again before transmitting. Considering this, this paper proposes a video watermarking algorithm in the bitstream domain based on moving object detection. To increase the robustness of the watermarking scheme, the moving macroblocks that belong to the moving object in each P frame can be identified via the moving object detection algorithm. Then, watermark embedding in the moving macroblocks is performed using codeword substitution to ensure the consistency of the bitstream. Moving object detection and watermark embedding are independent and are both carried out in the bitstream domain by partially decoding the bitstream; this avoids the complete decoding and reconstruction of the video, making the method to be highly efficient. The simulation results confirm that the proposed method is robust against recompression and has little impact on the video visual quality and no influence on the bit rate.
2021年02期 v.26 154-162页 [查看摘要][在线阅读][下载 24378K] [下载次数：36 ] |[网刊下载次数：0 ] |[引用频次：7 ] |[阅读次数：0 ] - Jiajun Gao;Fanzhang Li;Bangjun Wang;Helan Liang;
In this paper, we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML) that considers both global and local information. In this approach, we apply unlabeled training samples to study nonlinear manifold features, while considering global pairwise distances and maintaining local topology structure. Our method aims at minimizing global pairwise data distance errors as well as local structural errors. In order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data, we add a feature approximate error that can be used to learn a linear extractor. Also, we add a feature approximate error that can be used to learn a linear extractor. In addition, we use a method of adaptive neighbor selection to calculate local structural errors. This paper uses the kernel matrix method to optimize the original algorithm. Our algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.
2021年02期 v.26 163-171页 [查看摘要][在线阅读][下载 12372K] [下载次数：36 ] |[网刊下载次数：0 ] |[引用频次：5 ] |[阅读次数：0 ] - Jin Zhang;Zhaohui Tang;Yongfang Xie;Mingxi Ai;Weihua Gui;
Froth flotation is an important mineral concentration technique. Faulty conditions in flotation processes may cause the huge waste of mineral resources and reagents, and consequently, may lead to deterioration in terms of benefits of flotation plants. In this paper, we propose a computer vision-aided fault detection and diagnosis approach for froth flotation. Specifically, a joint Gabor texture feature based on the Copula model is designed to describe froth images; a rejection sampling technique is developed to generate training sets from the quality distribution of real flotation products, and then an isolation forest-based fault detector is learned; and a fault diagnosis model based on spline regression is developed for root cause identification. Simulation experiments conducted on the historical industry data show that the proposed strategy has better performance than the alternative methods. Thereafter, the entire framework has been tested on a lead-zinc flotation plant in China. Experimental results have demonstrated the effectiveness of the proposed method.
2021年02期 v.26 172-184页 [查看摘要][在线阅读][下载 22566K] [下载次数：29 ] |[网刊下载次数：0 ] |[引用频次：4 ] |[阅读次数：0 ] - Jie Hu;Yi Pan;Tianrui Li;Yan Yang;
In recent years, multi-view clustering research has attracted considerable attention because of the rapidly growing demand for unsupervised analysis of multi-view data in practical applications. Despite the significant advances in multi-view clustering, two challenges still need to be addressed, i.e., how to make full use of the consistent and complementary information in multiple views and how to discriminate the contributions of different views and features in the same view to efficiently reveal the latent cluster structure of multi-view data for clustering. In this study, we propose a novel Two-level Weighted Collaborative Multi-view Fuzzy Clustering(TW-Co-MFC) approach to address the aforementioned issues. In TW-Co-MFC, a two-level weighting strategy is devised to measure the importance of views and features, and a collaborative working mechanism is introduced to balance the within-view clustering quality and the cross-view clustering consistency. Then an iterative optimization objective function based on the maximum entropy principle is designed for multi-view clustering. Experiments on real-world datasets show the effectiveness of the proposed approach.
2021年02期 v.26 185-198页 [查看摘要][在线阅读][下载 1338K] [下载次数：33 ] |[网刊下载次数：0 ] |[引用频次：12 ] |[阅读次数：0 ]
- Bei Hui;Yanbo Liu;Jiajun Qiu;Likun Cao;Lin Ji;Zhiqiang He;
To grade Small Hepatocellular Car Cinoma(SHCC) using texture analysis of CT images, we retrospectively analysed 68 cases of Grade II(medium-differentiation) and 37 cases of Grades III and IV(high-differentiation).The grading scheme follows 4 stages:(1) training a Super Resolution Generative Adversarial Network(SRGAN)migration learning model on the Lung Nodule Analysis 2016 Dataset, and employing this model to reconstruct Super Resolution Images of the SHCC Dataset(SR-SHCC) images;(2) designing a texture clustering method based on Gray-Level Co-occurrence Matrix(GLCM) to segment tumour regions, which are Regions Of Interest(ROIs),from the original and SR-SHCC images, respectively;(3) extracting texture features on the ROIs;(4) performing statistical analysis and classifications. The segmentation achieved accuracies of 0.9049 and 0.8590 in the original SHCC images and the SR-SHCC images, respectively. The classification achived an accuracy of 0.838 and an Area Under the ROC Curve(AUC) of 0.84. The grading scheme can effectively reduce poor impacts on the texture analysis of SHCC ROIs. It may play a guiding role for physicians in early diagnoses of medium-differentiation and high-differentiation in SHCC.
2021年02期 v.26 199-207页 [查看摘要][在线阅读][下载 2449K] [下载次数：40 ] |[网刊下载次数：0 ] |[引用频次：7 ] |[阅读次数：0 ] - Kang Yang;Jinghua Zhu;Xu Guo;
With the booming of the Internet of Things(Io T) and the speedy advancement of Location-Based Social Networks(LBSNs), Point-Of-Interest(POI) recommendation has become a vital strategy for supporting people's ability to mine their POIs. However, classical recommendation models, such as collaborative filtering, are not effective for structuring POI recommendations due to the sparseness of user check-ins. Furthermore, LBSN recommendations are distinct from other recommendation scenarios. With respect to user data, a user's check-in record sequence requires rich social and geographic information. In this paper, we propose two different neural-network models,structural deep network Graph embedding Neural-network Recommendation system(SG-Neu Rec) and Deepwalk on Graph Neural-network Recommendation system(DG-Neu Rec) to improve POI recommendation. combined with embedding representation from social and geographical graph information(called SG-Neu Rec and DG-Neu Rec).Our model naturally combines the embedding representations of social and geographical graph information with user-POI interaction representation and captures the potential user-POI interactions under the framework of the neural network. Finally, we compare the performances of these two models and analyze the reasons for their differences. Results from comprehensive experiments on two real LBSNs datasets indicate the effective performance of our model.
2021年02期 v.26 208-218页 [查看摘要][在线阅读][下载 1125K] [下载次数：51 ] |[网刊下载次数：0 ] |[引用频次：11 ] |[阅读次数：0 ] - Ji Li;Akshita Maradapu Vera Venkata Sai;Xiuzhen Cheng;Wei Cheng;Zhi Tian;Yingshu Li;
The ever increasing requirements of data sensing applications result in the usage of Io T networks. These networks are often used for efficient data transfer. Wireless sensors are incorporated in the Io T networks to reduce the deployment and maintenance costs. Designing an energy efficient data aggregation method for sensor equipped Io T to process skyline query, is one of the most critical problems. In this paper, we propose two approximation algorithms to process the skyline query in wireless sensor networks. These two algorithms are uniform samplingbased approximate skyline query and Bernoulli sampling-based approximate skyline query. Solid theoretical proofs are provided to confirm that the proposed algorithms can yield the required query results. Experiments conducted on actual datasets show that the two proposed algorithms have high performance in terms of energy consumption compared to the simple distributed algorithm.
2021年02期 v.26 219-229页 [查看摘要][在线阅读][下载 1297K] [下载次数：26 ] |[网刊下载次数：0 ] |[引用频次：8 ] |[阅读次数：0 ] - Huan Yang;Feng Li;Dongxiao Yu;Yifei Zou;Jiguo Yu;
In the era of big data, sensor networks have been pervasively deployed, producing a large amount of data for various applications. However, because sensor networks are usually placed in hostile environments, managing the huge volume of data is a very challenging issue. In this study, we mainly focus on the data storage reliability problem in heterogeneous wireless sensor networks where robust storage nodes are deployed in sensor networks and data redundancy is utilized through coding techniques. To minimize data delivery and data storage costs, we design an algorithm to jointly optimize data routing and storage node deployment. The problem can be formulated as a binary nonlinear combinatorial optimization problem, and due to its NP-hardness, designing approximation algorithms is highly nontrivial. By leveraging the Markov approximation framework, we elaborately design an efficient algorithm driven by a continuous-time Markov chain to schedule the deployment of the storage node and corresponding routing strategy. We also perform extensive simulations to verify the efficacy of our algorithm.
2021年02期 v.26 230-238页 [查看摘要][在线阅读][下载 1423K] [下载次数：37 ] |[网刊下载次数：0 ] |[引用频次：13 ] |[阅读次数：0 ] - Ran Bi;Qian Liu;Jiankang Ren;Guozhen Tan;
Mobile-edge computing casts the computation-intensive and delay-sensitive applications of mobile devices onto network edges. Task offloading incurs extra communication latency and energy cost, and extensive efforts have focused on offloading schemes. Many metrics of the system utility are defined to achieve satisfactory quality of experience. However, most existing works overlook the balance between throughput and fairness. This study investigates the problem of finding an optimal offloading scheme in which the objective of optimization aims to maximize the system utility for leveraging between throughput and fairness. Based on Karush-Kuhn-Tucker condition, the expectation of time complexity is analyzed to derive the optimal scheme. A gradient-based approach for utility-aware task offloading is given. Furthermore, we provide an increment-based greedy approximation algorithm with 1+1/(e-1) ratio. Experimental results show that the proposed algorithms can achieve effective performance in utility and accuracy.
2021年02期 v.26 239-250页 [查看摘要][在线阅读][下载 629K] [下载次数：40 ] |[网刊下载次数：0 ] |[引用频次：13 ] |[阅读次数：0 ] 下载本期数据