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

Gypsum hydration: a theoretical and experimental study

Calcium sulphate dihydrate (CaSO4·2H2O or gypsum) is used widely as building\ud material because of its excellent fire resistance, aesthetics, and low price. Hemihydrate occurs in two formations of α- and β-type. Among them β-hemihydrate is mainly used to produce gypsum plasterboard since the hydration product of the α-hemihydrate is too brittle to be used as building material /10/. This article addresses the hydration of hemihydrate since it determines the properties of gypsum and it is influenced strongly by water and the properties of hemihydrate. The microstructure development of gypsum during hydration is investigated. The influence of water is studied from its effect on fresh behavior and void fraction of the gypsum

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

Solar neutrinos: the SNO salt phase results and physics of conversion

We have performed analysis of the solar neutrino data including results from the SNO salt phase as well as the combined analysis of the solar and the KamLAND results. The best fit values of neutrino parameters are Delta m^2 = 7.1e-5 eV^2, tan^2\theta = 0.40 with the boron flux f_B = 1.04. New SNO results strongly disfavor maximal mixing and the h-LMA region (Delta m^2 > 1e-4 eV^2) which is accepted now at the 3-sigma level. We find the 3-sigma upper bounds: Delta m^2 < 1.7e-4$ eV^2 and tan^2\theta < 0.64, and the lower bound Delta m^2 > 4.8e-5 eV^2. Non-zero 13-mixing does not change these results significantly. The present data determine quantitatively the physical picture of the solar neutrino conversion. At high energies relevant for SNO and Super-Kamiokande the deviation of the effective survival probability from the non-oscillatory value is about 10 - 14%. The oscillation effect contribution to this difference about 10% and the Earth regeneration is about 3 - 4%. At low energies (E < 1 MeV) the matter corrections to vacuum oscillation effect are below 5%. The predictions for the forthcoming measurements are given which include the spectral distortion and CC/NC ratio at SNO, the Day-Night asymmetry, the KamLAND spectrum and rate.Comment: figures and some numbers corrected, discussion of coherence loss added, number of pages slightly change

The Birkhoff theorem for unitary matrices of arbitrary dimensions

It was shown recently that Birkhoff's theorem for doubly stochastic matrices can be extended to unitary matrices with equal line sums whenever the dimension of the matrices is prime. We prove a generalization of the Birkhoff theorem for unitary matrices with equal line sums for arbitrary dimension.Comment: This manuscript presents a proof for the general unitary birkhoff theorem, conjectured in arXiv:1509.0862