R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections

The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits well in response to the rapid iteration of Android malware. The traditional solution for detecting Android malware requires continuous learning through pre-extracted features to maintain high performance of identifying the malware. In order to reduce the manpower of feature engineering prior to the condition of not to extract pre-selected features, we have developed a coloR-inspired convolutional neuRal networks (CNN)-based AndroiD malware Detection (R2-D2) system. The system can convert the bytecode of classes.dex from Android archive file to rgb color code and store it as a color image with fixed size. The color image is input to the convolutional neural network for automatic feature extraction and training. The data was collected from Jan. 2017 to Aug 2017. During the period of time, we have collected approximately 2 million of benign and malicious Android apps for our experiments with the help from our research partner Leopard Mobile Inc. Our experiment results demonstrate that the proposed system has accurate security analysis on contracts. Furthermore, we keep our research results and experiment materials on http://R2D2.TWMAN.ORG.Comment: Verison 2018/11/15, IEEE BigData 2018, Seattle, WA, USA, Dec 10-13, 2018. (Accepted

Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection

The advance of smartphones and cellular networks boosts the need of mobile advertising and targeted marketing. However, it also triggers the unseen security threats. We found that the phone scams with fake calling numbers of very short lifetime are increasingly popular and have been used to trick the users. The harm is worldwide. On the other hand, deceptive advertising (deceptive ads), the fake ads that tricks users to install unnecessary apps via either alluring or daunting texts and pictures, is an emerging threat that seriously harms the reputation of the advertiser. To counter against these two new threats, the conventional blacklist (or whitelist) approach and the machine learning approach with predefined features have been proven useless. Nevertheless, due to the success of deep learning in developing the highly intelligent program, our system can efficiently and effectively detect phone scams and deceptive ads by taking advantage of our unified framework on deep neural network (DNN) and convolutional neural network (CNN). The proposed system has been deployed for operational use and the experimental results proved the effectiveness of our proposed system. Furthermore, we keep our research results and release experiment material on http://DeceptiveAds.TWMAN.ORG and http://PhoneScams.TWMAN.ORG if there is any update.Comment: 6 pages, TAAI 2017 versio

Three essays on spatial econometrics with an emphasis on testing

Spatial Modeling has been one of the important parts in Applied Econometrics as well as Econometrics Theory in the past thirty years, not only because of the nature that the geographic locations and interactions play a crucial role in forming behavior, but also because of the challenging problems inherited from spatial dependence in Econometric models. Misspecifications of spatial dependence in regression models lead to misleading inferences and policy implications. In this dissertation I focus on issues of model specification tests which arise from the spatial structures of the data, and it contributes to the Spatial Econometric literature in two ways: first, the important consequences of misspecified spatial dependence in estimation, hypothesis testing, and calculation of impact effects, and second, the methodologies for non-standard tests in spatial regression models. I provide both econometric methods and empirical examples to demonstrate the usefulness of the proposed testing procedures. In chapter 1 I study the behavior of standard and adjusted Rao score (RS) tests for spatial dependence in presence of negative spatial dependence. I found that the power of the standard test can be very low when there is negative spatial dependence. I also compared the features of negative autocorrelation between the time series and spatial contexts. In time series case, both the pattern of variance-covariance matrices and the power curves are symmetric for positive and negative serial correlations. This symmetry, however, is not observed in the spatial context. I applied my findings to the U.S. state government expenditure data, and found negative spatial lag dependence in U.S. state government expenditure, suggesting competitions among the state governments [Saavedra (2000); Boarnet, Marlon and Glazer (2002)]. Consistent with my theoretical derivation, the standard RS test is misleading, and under the negative spatial dependence, the values and interpretation of impact effects are also different. When incorporating spatial dependence, the most common specification is a spatial autoregressive (AR) process, either in the dependent variable or disturbances. However, as argued in Anselin (2003), in many cases a spatial moving average (MA) is more appropriate if the mechanism of interest is a localized spatial spillover. In chapter 2 I consider the problem of testing no spatial dependence against a spatial autoregressive and moving average (ARMA) process, which allows for a global direct spatial effect in the dependent variable as well as an unobserved or indirect local spatial effect. I suggest a test procedure and the simulation results show that the proposed test has desired size and good power performance. In chapter 3, I further study the problems of testing no spatial dependence against a spatial ARMA process in the disturbances, in the presence of spatial lag dependence. The problems of conducting such a test are twofold. First, under the null hypothesis of no spatial dependence in the disturbances, one underlying nuisance parameter is not identified. Besides, the possible presence of spatial lag dependence may affect the performance of the test. To deal with this twin-problem of nuisance parameters simultaneously, I apply the Davies (1977, 1987) procedure to the adjusted RS statistic [Anselin, Bera, Florax, and Yoon (1996)]. I conducted extensive Monte Carlo experiments to study the finite sample performance of my proposed test, and found my test has very good size and power properties in small samples and performs very well compared to other conventional RS tests. Finally I applied the test to a number of real data sets, such as the Columbus crime data [Anselin (1988); Anselin et all (1996); Sen, Bera, and Kao (2012)], Boston housing market data [Harrison and Rubinfeld (1978); Pace and Gilley (1997)], and Netherland investment data [Florax (1992); Anselin et all (1996)]. The empirical results clearly demonstrate the effectiveness of my test and the shortcomings of currently available tests

Bending Stress Analysis of Laminated Foldable Touch Panel

AbstractThe touch panel technology has been developed in recent years, and the foldable touch panel is one of the newly attractive characteristics. This article focuses on the bending stress analysis of foldable touch panel, composed of plastic substrate PET, adhesive layer, plastic layer PI, organic layer and conductive layer ITO to form a seven-layer laminated structure. By applying four-point bending, the stress distribution of the touch panel under different radius of curvature was analyzed. The results show that the maximum von Mises stress occurred in the ITO layer and the maximum von Mises stress increased from 0.497GPa to 1.242GPa with decreasing radius of curvature. The region near the center of the touch panel has higher von Mises stress, and the relation between the radius of curvature and the maximum von Mises stress exhibits a non-linear feature

Risk factors for subsidence in anterior cervical fusion with stand-alone polyetheretherketone (PEEK) cages: a review of 82 cases and 182 levels

INTRODUCTION: To determine risk factors for subsidence in patients treated with anterior cervical discectomy and fusion (ACDF) and stand-alone polyetheretherketone (PEEK) cages. MATERIALS AND METHODS: Records of patients with degenerative spondylosis or traumatic disc herniation resulting in radiculopathy or myelopathy between C2 and C7 who underwent ACDF with stand-alone PEEK cages were retrospectively reviewed. Cages were filled with autogenous cancellous bone harvested from iliac crest or hydroxyapatite. Subsidence was defined as a decrease of 3 mm or more of anterior or posterior disc height from that measured on the postoperative radiograph. Eighty-two patients (32 males, 50 females; 182 treatment levels) were included in the analysis. RESULTS: Most patients had 1–2 treatment levels (62.2 %), and 37.8 % had 3–4 treatment levels. Treatment levels were from C2–7. Of the 82 patients, cage subsidence occurred in 31 patients, and at 39 treatment levels. Multivariable analysis showed that subsidence was more likely to occur in patients with more than two treatment levels, and more likely to occur at treatment levels C5–7 than at levels C2–5. Subsidence was not associated with postoperative alignment change but associated with more disc height change (relatively oversized cage). CONCLUSION: Subsidence is associated with a greater number of treatment levels, treatment at C5–7 and relatively oversized cage use

A Study of the Talent Training Project Management for Semiconductor Industry in Taiwan: The Application of a Hybrid Data Envelopment Analysis Approach

The purpose of this study is to evaluate the training institution performance and to improve the management of the Manpower Training Project (MTP) administered by the Semiconductor Institute in Taiwan. Much literature assesses the efficiency of an internal training program initiated by a firm, but only little literature studies the efficiency of an external training program led by government. In the study, a hybrid solution of ICA-DEA and ICA-MPI is developed for measuring the efficiency and the productivity growth of each training institution over the period. The technical efficiency change, the technological change, pure technical efficiency change, scale efficiency change, and the total factor productivity change were evaluated according to five inputs and two outputs. According to the results of the study, the training institutions can be classified by their efficiency successfully and the guidelines for the optimal level of input resources can be obtained for each inefficient training institution. The Semiconductor Institute in Taiwan can allocate budget more appropriately and establish withdrawal mechanisms for inefficient training institutions

α-Lactosylceramide Protects Against iNKT-Mediated Murine Airway Hyperreactivity and Liver Injury Through Competitive Inhibition of Cd1d Binding.

Invariant natural killer T (iNKT) cells, which are activated by T cell receptor (TCR)-dependent recognition of lipid-based antigens presented by the CD1d molecule, have been shown to participate in the pathogenesis of many diseases, including asthma and liver injury. Previous studies have shown the inhibition of iNKT cell activation using lipid antagonists can attenuate iNKT cell-induced disease pathogenesis. Hence, the development of iNKT cell-targeted glycolipids can facilitate the discovery of new therapeutics. In this study, we synthesized and evaluated α-lactosylceramide (α-LacCer), an α-galactosylceramide (α-GalCer) analog with lactose substitution for the galactose head and a shortened acyl chain in the ceramide tail, toward iNKT cell activation. We demonstrated that α-LacCer was a weak inducer for both mouse and human iNKT cell activation and cytokine production, and the iNKT induction by α-LacCer was CD1d-dependent. However, when co-administered with α-GalCer, α-LacCer inhibited α-GalCer-induced IL-4 and IFN-γ production from iNKT cells. Consequently, α-LacCer also ameliorated both α-GalCer and GSL-1-induced airway hyperreactivity and α-GalCer-induced neutrophilia when co-administered in vivo. Furthermore, we were able to inhibit the increases of ConA-induced AST, ALT and IFN-γ serum levels through α-LacCer pre-treatment, suggesting α-LacCer could protect against ConA-induced liver injury. Mechanistically, we discerned that α-LacCer suppressed α-GalCer-stimulated cytokine production through competing for CD1d binding. Since iNKT cells play a critical role in the development of AHR and liver injury, the inhibition of iNKT cell activation by α-LacCer present a possible new approach in treating iNKT cell-mediated diseases