Geodesic Distance Function Learning via Heat Flow on Vector Fields

Learning a distance function or metric on a given data manifold is of great importance in machine learning and pattern recognition. Many of the previous works first embed the manifold to Euclidean space and then learn the distance function. However, such a scheme might not faithfully preserve the distance function if the original manifold is not Euclidean. Note that the distance function on a manifold can always be well-defined. In this paper, we propose to learn the distance function directly on the manifold without embedding. We first provide a theoretical characterization of the distance function by its gradient field. Based on our theoretical analysis, we propose to first learn the gradient field of the distance function and then learn the distance function itself. Specifically, we set the gradient field of a local distance function as an initial vector field. Then we transport it to the whole manifold via heat flow on vector fields. Finally, the geodesic distance function can be obtained by requiring its gradient field to be close to the normalized vector field. Experimental results on both synthetic and real data demonstrate the effectiveness of our proposed algorithm

Benchmarking insider threat intrusion detection systems

viii, 97 leaves : ill. ; 29 cm.Includes abstract.Includes bibliographical references (leaves 88-97).An intrusion detection system generally detects unwanted manipulations to computer systems. In recent years, this technology has been used to protect personal information after it has been collected by an organization. Selecting an appropriate IDS is an important decision for system security administrators, to keep authorized employees from abusing their access to the system to exploit sensitive information. To date, little work has been done to create a benchmark for small and mid-size organizations to measure and compare the capability of different insider threat IDSs which are based on user profiling. It motivates us to create a benchmark which enables organizations to compare these different IDSs. The benchmark is used to produce useful comparisons of the accuracy and overhead of two key research implementations of future insider threat intrusion algorithms, which are based on user behavior

Fault diagnosis of motorized spindle via modified empirical wavelet transform-kernel PCA and optimized support vector machine

The fault diagnosis of motorized spindle contributes to the improvement of the reliability of computer numerical control machine tools. Presently, numerous mechanical fault diagnosis technologies suffer from the drawbacks of mode mixing, non-adaptive analysis, and low efficiency. Therefore, adopting an effective signal processing method for fault diagnosis of motorized spindle is essential. A method based on modified empirical wavelet transform (EWT) and kernel principal component analysis (Kernel PCA) is proposed. A new method, which determines the proper number of the Fourier spectrum segments, is applied when using EWT. To improve computational efficiency, Kernel PCA is adopted to reduce dimension. The support vector machine optimized by genetic algorithm is introduced to accomplish fault identification. The performance of the proposed method is validated through single and compound fault experiments. Results show that the recognition rate using the proposed method reached 98.8095 % and 98.4375 % in terms of single and compound fault diagnoses, respectively. Moreover, compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), local mean decomposition (LMD) and EWT, the proposed method can save much computing time. The proposed method can be generalized to other mechanical fault diagnoses as well

A rolling bearing fault diagnosis method based on VMD – multiscale fractal dimension/energy and optimized support vector machine

To achieve the goal of automated rolling bearing fault diagnosis, a variational mode decomposition (VMD) based diagnosis scheme was proposed. VMD was firstly used to decompose the vibration signals into a series of band-limited intrinsic mode functions (BLIMFs). Subsequently, the multiscale fractal dimension (MSFD) and multiscale energy (MSEN) of each BLIMF were calculated and combined together as features of the original vibration signals. In an attempt to accelerate the classification speed, one-way analysis of variance (ANOVA) test was adopted to extract significant features from the redundant features. Finally, those significant features were fed into the optimized support vector machine (SVM), which was optimized by the genetic algorithm (GA), for classification. Experimental results on the international public Case Western Reserve University bearing data indicate the effectiveness of the proposed method with a classification accuracy of 99.75 % for seven classes. Moreover, our approach also shows good anti-noise performance in different signal-to-noise ratios (SNRs)

Prevalence of Common Respiratory Viral Infections and Identification of Adenovirus in Hospitalized Adults in Harbin, China 2014 to 2017

Background: Respiratory infections pose a great challenge in global health, and the prevalence of viral infection in adult patients has been poorly understood in northeast China. Harbin is one of the major cities in northeast China, and more than half of any given year in Harbin is occupied by winter. To reveal the viral etiology and seasonality in adult patients from Harbin, a 4-year consecutive survey was conducted in Harbin, China.Methods: From January 2014 to December 2017, specimens were obtained from adult patients admitted to the Second Affiliated Hospital of Harbin Medical University with lower respiratory tract infections. Sputum samples were examined by direct immunofluorescence assays to detect seven common respiratory viruses, including influenza virus (type A and B), parainfluenza virus (type 1 to 3), respiratory syncytial virus and adenovirus. Adenovirus positive samples were seeded onto A549 cells to isolate viral strains. Phylogenetic analysis was conducted on the highly variable region of adenoviral hexon gene.Results: A total of 1,300 hospitalized adult patients with lower respiratory tract infections were enrolled, in which 189 patients (14.5%) were detected as having at least one viral infection. The co-infection rate in this study was 25.9% (49/189). The dominant viral pathogen from 2014 to 2017 was parainfluenza virus, with a detection rate of 7.2%, followed by influenza virus, respiratory syncytial virus and adenovirus. Based on the climate seasons determined by daily average temperature, the highest overall viral detection rate was detected in spring (22.0%, 52/236), followed by winter (13.4%, 109/813), autumn (11.4%, 13/114) and summer (10.9%, 15/137). Adenovirus type 3 strains with slight variations were isolated from positive cases, which were closely related to the GB strain from the United States, as well as the Harbin04B strain isolated locally.Conclusion: This study demonstrated that common respiratory viruses were partially responsible for hospitalized lower respiratory tract infections in adult patients from Harbin, China, with parainfluenza virus as the dominant viral pathogen. Climate seasons could be rational indicators for the seasonality analysis of airborne viral infections. Future surveillance on viral mutations would be necessary to reveal the evolutionary history of respiratory viruses