Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks

The traffic classification based on the network applications is one important issue for network management. In this paper, we propose an application-based online and offline traffic classification, based on deep learning mechanisms, over software-defined network (SDN) testbed. The designed deep learning model, resigned in the SDN controller, consists of multilayer perceptron (MLP), convolutional neural network (CNN), and Stacked Auto-Encoder (SAE), in the SDN testbed. We employ an open network traffic dataset with seven most popular applications as the deep learning training and testing datasets. By using the TCPreplay tool, the dataset traffic samples are re-produced and analyzed in our SDN testbed to emulate the online traffic service. The performance analyses, in terms of accuracy, precision, recall, and F1 indicators, are conducted and compared with three deep learning models

An Accelerating 3D Image Reconstruction System Based on the Level-of-Detail Algorithm

This paper proposes a research of An Accelerating 3D Image Reconstruction System Based on the Level-of-Detail Algorithm and combines 3D graphic application interfaces, such as DirectX3D and OpenCV to reconstruct the 3D imaging system for Magnetic Resonance Imaging (MRI), and adds Level of Detail (LOD) algorithm to the system. The system uses the volume rendering method to perform 3D reconstruction for brain imaging. The process, which is based on the level of detail algorithm that converts and formulates functions from differing levels of detail and scope, significantly reduces the complexity of required processing and computation, under the premises of maintaining drawing quality. To validate the system's efficiency enhancement on brain imaging reconstruction, this study operates the system on various computer platforms, and uses multiple sets of data to perform rendering and 3D object imaging reconstruction, the results of which are then verified and compared

Assessment of latent tuberculosis infection in psychiatric inpatients: A survey after tuberculosis outbreaks

AbstractBackground/PurposeTo investigate risk factors of latent tuberculosis infection (LTBI) among inpatients of chronic psychiatric wards with tuberculosis (TB) outbreaks.MethodsIn April 2013, inpatients of four all-male wards with TB outbreaks were tested for LTBI using the QuantiFERON-TB Gold in Tube (QFT) method. Based on this investigation, a retrospective study was conducted to assess risk factors for LTBI. Inpatients exposed to cluster-A or cluster-B TB cases were defined as contacts of cluster-A or cluster-B, and others, as nonclustered contacts.ResultsAmong 355 inpatients with TB exposure, 134 (38%) were QFT-positive for LTBI. Univariate analysis showed that significant predictors for QFT-positivity were age, case-days of exposure to all TB cases (TB-all) and to sputum smear positive cases, number of source cases with cough, and exposure to cluster-A TB cases. Independent risk factors for LTBI were higher age [adjusted odds ratio (OR) 1.03, 95% confidence intervals (CI: 1.01–1.05)], TB-all exposure case-days ≥ 200 [adjusted OR 2.04 (1.06–3.92)] and exposure to cluster-A TB cases [adjusted OR 2.82 (1.30–6.12)] after adjustment for the sputum smear positivity, and cough variables of the source cases. The contacts of cluster-A had a greater risk of LTBI than did those of cluster-B, especially in the younger population (≤50 years) after adjustment [adjusted OR 2.64 (1.03–6.76)].ConclusionAfter TB outbreaks, more than one third of inpatients were QFT-positive for LTBI. Our findings suggest that, beside the infectiousness of source cases, intensity of exposure, and age of contacts, exposure to TB cases in potential genotyping clusters may be predictive for LTBI in this male psychiatric population

Detection of genetic and epigenetic DNA markers in urine for the early detection of primary and recurrent hepatocellular carcinoma

Poster presented at American Association of the Study of Liver Diseases (AASLD) meeting in San Francisco California. Objective: Develop a urine test using a panel of select genetic and epigenetic markers for the early detection of primary and recurrent HCC. Introduction: Hepatocellular carcinoma (HCC) or liver cancer is an aggressive disease and one of the fastest growing cancers by incidence in the United States. Early detection is the key for effective treatment of HCC as the 5-year survival rate is 26% in early stage HCC as compared to only 2% when found after spreading to distant organs. The current marker, alpha-feto protein (AFP) and its fucosylated glycoform, L3, are of limited value with only 40-60% sensitivity.https://jdc.jefferson.edu/gastrohepposters/1000/thumbnail.jp

Inhibitory Effects of Terminalia catappa on UVB-Induced Photodamage in Fibroblast Cell Line

This study investigated whether Terminalia catappa L. hydrophilic extract (TCLW) prevents photoaging in human dermal fibroblasts after exposure to UVB radiation. TCLW exhibited DPPH free radical scavenging activity and protected erythrocytes against AAPH-induced hemolysis. In the gelatin digestion assay, the rates of collagenase inhibition by TCL methanol extract, TCLW, and its hydrolysates were greater than 100% at the concentration of 1 mg/mL. We found that serial dilutions of TCLW (10–500 μg/mL) inhibited collagenase activity in a dose-dependent manner (82.3% to 101.0%). However, TCLW did not significantly inhibit elastase activity. In addition, TCLW inhibited MMP-1 and MMP-9 protein expression at a concentration of 25 μg/mL and inhibited MMP-3 protein expression at a concentration of 50 μg/mL. TCLW also promoted the protein expression of type I procollagen. We also found that TCLW attenuated the expression of MMP-1, -3, and -9 by inhibiting the phosphorylation of ERK, JNK, and p38. These findings suggest that TCLW increases the production of type I procollagen by inhibiting the activity of MMP-1, -3 and -9, and, therefore, has potential use in anti-aging cosmetics

DEVELOPMENT AND VALIDATION OF THE TAIWAN CHILDREN’S ENVIRONMENTAL ACTION INDEX

In this study, we use Smith-Sebasto & Fortner’s (1994) Environmental Action Internal Control Index (EAICI) as a framework to develop, and validate a useful instrument for assessing environmental attitudes and behavior among elementary and middle school children within the Taiwanese context. We dub the new instrument the Taiwan Children’s Environmental Action Index (TCEAI). Our findings suggest that the TCEAI displays substantial internal consistency (Cronbach's alpha=.92), moderately positive correlations with self-report measures of environmentally responsible behavior (R = .35 to .46, p < .01), and few threats to validity by age or gender. The results suggest that the TCEAI may be used to elicit important dimensions of environmental attitudes and to predict environmentally responsible behavior for elementary and middle school children in Taiwan. Practical implications are discussed

Randomized Comparative Study of the Effects of Treatment with Once-Daily, Niacin Extended-Release/Lovastatin and with Simvastatin on Lipid Profile and Fibrinolytic Parameters in Taiwan

Hyperlipidemia can be effectively treated either with niacin or HMG-CoA reductase inhibitor (statin), or a combination of both. Few reports showed the effects of the combination regimen with niacin and statin on hemostatic functions. We conducted a single-center, double-blind, double-dummy, randomized, two-arm study to assess the effects of the niacin extended-release/lovastatin therapy in a fixed-dose formulation and of simvastatin on lipid lowering and two fibrinolytic parameters, fibrinogen and d-dimer. All patients were enrolled according to NCEP-ATP III guidelines and underwent a placebo run-in period of 4 weeks before being randomized to either niacin extended-release/lovastatin tablets (500/20 mg) once daily (n = 36) or simvastatin capsule (20 mg) once daily (n = 34). After 16 weeks of treatment, both groups of patients showed significantly reduced low-density lipoprotein cholesterol and total cholesterol (LDL-C, p < 0.001 and < 0.001, respectively, p = 0.159 between the groups; TC, p < 0.001 and < 0.001, respectively, p = 0.018 between the groups). Both drugs were well tolerated. Only in the group treated with niacin extended-release/lovastatin was fibrinogen concentration significantly reduced after treatment (2.48 ± 0.65 to 1.99 ± 0.62 g/L, p = 0.008). No difference was found with d-dimer in either group. This study shows that both niacin extended-release/ lovastatin and simvastatin are effective and well-tolerated lipid-lowering drugs in Taiwanese patients with dyslipidemia. A combinational treatment with niacin extended-release/lovastatin may provide additional benefit in fibrinolysis

Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine

In this paper, we propose a robust tactile sensing image recognition scheme for automatic robotic assembly. First, an image reprocessing procedure is designed to enhance the contrast of the tactile image. In the second layer, geometric features and Fourier descriptors are extracted from the image. Then, kernel principal component analysis (kernel PCA) is applied to transform the features into ones with better discriminating ability, which is the kernel PCA-based feature fusion. The transformed features are fed into the third layer for classification. In this paper, we design a classifier by combining the multiple kernel learning (MKL) algorithm and support vector machine (SVM). We also design and implement a tactile sensing array consisting of 10-by-10 sensing elements. Experimental results, carried out on real tactile images acquired by the designed tactile sensing array, show that the kernel PCA-based feature fusion can significantly improve the discriminating performance of the geometric features and Fourier descriptors. Also, the designed MKL-SVM outperforms the regular SVM in terms of recognition accuracy. The proposed recognition scheme is able to achieve a high recognition rate of over 85% for the classification of 12 commonly used metal parts in industrial applications