Fine-Grained Binary Analysis Method for Privacy Leakage Detection on the Cloud Platform

Nowadays cloud architecture is widely applied on the internet.New malware aiming at the privacy data stealing or crypto currency mining is threatening the security of cloud platforms.In view of the problems with existing application behavior monitoring methods such as coarse-grained analysis,high performance overhead and lack of applicability,this paper proposes a new fine-grained binary program monitoring and analysis method based on multiple system level components,which is used to detect the possible privacy leakage of applications installed on cloud platforms.It can be used online in cloud platform environments for fine-grained automated analysis of target programs,ensuring the stability and continuity of program execution.We combine the external interception and internal instrumentation and design a variety of optimization schemes to further reduce the impact of fine-grained analysis on the performance of target programs,enabling it to be employed in actual environments.The experimental results show that the proposed method is feasible and can achieve the acceptable analysis performance while consuming a small amount of system resources.The optimization schemes can go beyond traditional dynamic instrumentation methods with better analytical performance and can be more applicable to online analysis on cloud platforms.

Photometric solution and period analysis of the contact binary system AH Cnc

Photometric observations of AH Cnc, a W UMa-type system in the open cluster M67, were car- fled out by using the 50BIN telescope. About 100h of time-series/3- and V-band data were taken, based on which eight new times of light minima were determined. By applying the Wilson-Devinney method, the light curves were modeled and a revised photometric solution of the binary system was derived. We con- firmed that AH Cnc is a deep contact （f = 51%）, low mass-ratio （q - 0.156） system. Adopting the distance modulus derived from study of the host cluster, we have re-calculated the physical parameters of the binary system, namely the masses and radii. The masses and radii of the two components were estimated to be respectively 1.188（4-0.061） Me, 1.332（4-0.063） RQ for the primary component and 0.185（4-0.032） Me, 0.592（4-0.051） Re for the secondary. By adding the newly derived minimum timings to all the available data, the period variations of AH Cnc were studied. This shows that the orbital period of the binary is con- tinuously increasing at a rate of dp/dt = 4.29 x 10-10 d yr-1. In addition to the long-term period increase, a cyclic variation with a period of 35.26 yr was determined, which could be attributed to an unresolved tertiary component of the system.

SHFuzz:A Hybrid Fuzzing Method Assisted by Static Analysis for Binary Programs

Fuzzing is an effective technique to find security bugs in programs by quickly exploring the input space of programs.To further discover vulnerabilities hidden in deep execution paths,the hybrid fuzzing combines fuzzing and concolic execution for going through complex branch conditions.In general,we observe that the execution path which comes across more and complex basic blocks may have a higher chance of containing a security bug.Based on this observation,we propose a hybrid fuzzing method assisted by static analysis for binary programs.The basic idea of our method is to prioritize seed inputs according to the complexity of their associated execution paths.For this purpose,we utilize static analysis to evaluate the complexity of each basic block and employ the hardware trace mechanism to dynamically extract the execution path for calculating the seed inputs’weights.The key advantage of our method is that our system can test binary programs efficiently by using the hardware trace and hybrid fuzzing.To evaluate the effectiveness of our method,we design and implement a prototype system,namely SHFuzz.The evaluation results show SHFuzz discovers more unique crashes on several real-world applications and the LAVA-M dataset when compared to the previous solutions.

A Local Binary Pattern-Based Method for Color and Multicomponent Texture Analysis

Local Binary Patterns (LBPs) have been highly used in texture classification for their robustness, their ease of implementation and their low computational cost. Initially designed to deal with gray level images, several methods based on them in the literature have been proposed for images having more than one spectral band. To achieve it, whether assumption using color information or combining spectral band two by two was done. Those methods use micro structures as texture features. In this paper, our goal was to design texture features which are relevant to color and multicomponent texture analysis without any assumption. Based on methods designed for gray scale images, we find the combination of micro and macro structures efficient for multispectral texture analysis. The experimentations were carried out on color images from Outex databases and multicomponent images from red blood cells captured using a multispectral microscope equipped with 13 LEDs ranging from 375 nm to 940 nm. In all achieved experimentations, our proposal presents the best classification scores compared to common multicomponent LBP methods. 99.81%, 100.00%, 99.07% and 97.67% are maximum scores obtained with our strategy respectively applied to images subject to rotation, blur, illumination variation and the multicomponent ones.

The Spectrum Analysis of Three Chromospherically Active Binary Stars

We present the research results on new CCD spectroscopic observations of three chromospherically active binary stars (BY Dra class), which were obtained by means of Coude echelle spectrograph fed by the 2 16m telescope at Beijing Astronomical Observatory. These spectrum images were reduced according to the standard fashion using IRAF package. With the aid of stellar model atmosphere, we have analyzed these spectra and derived the average metal abundance and Li abundance of three systems. Using two special spectral lines, we have also discussed the chromospheric activity indicators of them.

Android Malware Detection Using Local Binary Pattern and Principal Component Analysis

Nowadays,analysis methods based on big data have been widely used in malicious software detection.Since Android has become the dominator of smartphone operating system market,the number of Android malicious applications are increasing rapidly as well,which attracts attention of malware attackers and researchers alike.Due to the endless evolution of the malware,it is critical to apply the analysis methods based on machine learning to detect malwares and stop them from leakaging our privacy information.In this paper,we propose a novel Android malware detection method based on binary texture feature recognition by Local Binary Pattern and Principal Component Analysis,which can visualize malware and detect malware accurately.Also,our method analyzes malware binary directly without any decompiler,sandbox or virtual machines,which avoid time and resource consumption caused by decompiler or monitor in this process.Experimentation on 5127 benigns and 5560 malwares shows that we obtain a detection accuracy of 90%.

Timescale Analysis of Spectral Lags

A technique for timescale analysis of spectral lags performed directly in the time domain is developed. Simulation studies are made to compare the time domain technique with the Fourier frequency analysis for spectral time lags. The time domain technique is applied to studying rapid variabilities of X-ray binaries and γ-ray bursts. The results indicate that in comparison with the Fourier analysis the timescale analysis technique is more powerful for the study of spectral lags in rapid variabilities on short time scales and short duration flaring phenomena.

Quantum Biophysics of the Atmosphere: Factor Analysis of the Annual Dynamics of Maximum, Minimum and Average Temperatures from 1879 to 2017 to Hadley English Temperature Center (Hadcet)

Factor analysis of annual dynamics from 1879 to 2017 was carried out by the method of identification of stable regularities:maximum,minimum and average air temperature of Central England according to HadCET.The sample capacity was 139 rows.In factor analysis,time is excluded,and it acts only as a system-forming factor that ensures the relationship between the three parameters of climate and weather.Therefore,the ade­quacy of the dynamics models is taken into account in the diagonal cells of the correlation matrix.In addition to time,different lists of objects are possible in factor analysis.The coefficient of correlation variation,that is,a measure of the functional relationship between the parameters of the system(annual weather at the weather station in Central England)is 0.8230 for trends,0.8603 taking into account the annual dynamics of the four-membered model obtained from the computational capabilities of the software environment CurveExpert-1.40,and 0.9578 for the full up to the error of measurement wavelet analysis of the dynamics of the values of three factors.In all three methods of factor analysis,the meteorolog­ical parameter«average Annual temperature»was in the first place as the influencing variable,the«Maximum temperature»was in the second place,and the«Minimum temperature»was in the third place.As the dependent measure in these areas there are three kinds of temperature.The comparison shows that among the binary relations between the three temperatures,the average temperature on the maximum air temperature in the surface layer of the atmosphere has the greatest influence on the correlation coefficient 0.9765.At the same time,all six equations refer to strong connections,so there is a high quantum certainty between the three types of temperature.But when predicting the most meaningful essence showed the maximum temperature.

Thermodynamic analysis of the Ti-Al-N system

Motivated by the application of （Ti, Al）N alloy compound in the coating layer, the ternary phase diagram of Ti-Al-N was analyzed by the calculation of the phase diagram （CALPHAD） technique. The isothermal sections of the Ti-Al-N ternary system were constructed and compared with the literature experimental results. The thermodynamic parameters of the Ti-Al-N ternary system and the related Ti-N and Al-N binary systems were adopted from literatures, whereas, those of the Ti-Al binary from the literatures were adjusted according to both the ternary and the binary phase equilibria. The consistency between the calculated results and the experimental data shows that considering the ternary thermodynamic relationship, the adjustments to the thermodynamic parameters of the related binaries are necessary.

Fault Tree Analysis of CNC Machine Tool Based on BDD Technology

CNC machine tool is a large complex system which contains both mechanical and electrical components.As one of these components,the spindle is crucial for the performance of CNC machine tool.To improve the quality of CNC machine,the reliability of spindle was evaluated in this paper using a fault tree analysis(FTA)method.The FTA method is a set of calculation methods based on Boolean algebra.However,it is difficult to analyze a large and complex fault tree with inaccurate results and low efficiency as well as the complexity of time and space.Both of them will result in the so-called "combinatorial explosion".To overcome this problem,the analysis method based on binary decision diagram(BDD)was introduced in our works,and a sorting method about bottom events was also recommended which can reduce the size of the BDD effectively.

Identification of variety of rice and determination of content of each rice in mixtures of two rices by nonlinear chemical analysis

Fingerprints of two varieties of rice and their mixtures were investigated by a nonlinear chemical reaction system consisting of rice components,sodium bromate,manganese sulfate,sulfuric acid and acetone.The variety of rice was identified by the visual characteristic of fingerprint and system similarity pattern recognition,and the content of each variety of rice in the mixture was determined by the quantitative information of fingerprint.The results show that nonlinear chemical analysis may be used to exactly identify the variety of pure rice and to accurately determine the content of each variety of rice in the mixture,indicating the method is simple and convenient.

BOOTSTRAP TECHNIQUE FOR ROC ANALYSIS: A STABLE EVALUATION OF FISHER CLASSIFIER PERFORMANCE

This paper presents a novel bootstrap based method for Receiver Operating Characteristic (ROC) analysis of Fisher classifier. By defining Fisher classifier’s output as a statistic, the bootstrap technique is used to obtain the sampling distributions of the outputs for the positive class and the negative class respectively. As a result, the ROC curve is a plot of all the (False Positive Rate (FPR), True Positive Rate (TPR)) pairs by varying the decision threshold over the whole range of the boot- strap sampling distributions. The advantage of this method is, the bootstrap based ROC curves are much stable than those of the holdout or cross-validation, indicating a more stable ROC analysis of Fisher classifier. Experiments on five data sets publicly available demonstrate the effectiveness of the proposed method.

The Establishment of Mathematical Models for the Composition Analysis and Identification of Ancient Glass Products

Glass is the precious material evidence of the trade of the early Silk Road. The ancient glass was easily affected by the environmental impact and weathering, and the change of composition ratios affected the correct judgment of its category. In this paper, mathematical models and methods such as Chi-square test, weighted average method, principal component analysis, cluster analysis, binary classification model and grey correlation analysis were used comprehensively to analyze the data of sample glass products combined with their categories. The results showed that the weathered high-potassium glass could be divided into 12, 9, 10 and 27, 7, 22 and so on.

The W UMa binaries USNO-A2.0 1350-17365531,V471 Cas,V479 Lac and V560 Lac:light curve solutions and global parameters based on Gaia distances

We present photometric observations in Sloan filters g′, i′of the eclipsing W UMa stars USNOA2.0 1350-17365531, V471 Cas, V479 Lac and V560 Lac. The sinusoidal-like O-C diagram of V471 Cas indicates the presence of a third body with mass 0.12 M_⊙(a red dwarf) at distance 897 R_⊙. The O-C diagram of V479 Lac reveals a period decrease of d P/dt =-1.69 × 10-6d yr-1. The results of the light curve solutions are:(i) the targets are overcontact binaries with small fill-out factors;(ii) their components are F–K stars, comparable in size, whose temperature differences are below 80 K;(iii) all targets undergo partial eclipses and to limit the possible mass ratios we carried out two-step q-search analysis. The target global parameters(luminosities, radii, masses) were obtained on the basis of their Gaia distances and the results of our light curve solutions. The obtained total mass of V560 Lac turns out to be smaller than the lower mass limit for presently known W UMa binaries of 1.0-1.2 M_⊙, i.e. this target is a peculiar overcontact system.

Variant Map System to Simulate Complex Properties of DNA Interactions Using Binary Sequences

Stream cipher, DNA cryptography and DNA analysis are the most important R&D fields in both Cryptography and Bioinformatics. HC-256 is an emerged scheme as the new generation of stream ciphers for advanced network security. From a random sequencing viewpoint, both sequences of HC-256 and real DNA data may have intrinsic pseudo-random properties respectively. In a recent decade, many DNA sequencing projects are developed on cells, plants and animals over the world into huge DNA databases. Researchers notice that mammalian genomes encode thousands of large noncoding RNAs (lncRNAs), interact with chromatin regulatory complexes, and are thought to play a role in localizing these complexes to target loci across the genome. It is a challenge target using higher dimensional visualization tools to organize various complex interactive properties as visual maps. The Variant Map System (VMS) as an emerging scheme is systematically proposed in this paper to apply multiple maps that used four Meta symbols as same as DNA or RNA representations. System architecture of key components and core mechanism on the VMS are described. Key modules, equations and their I/O parameters are discussed. Applying the VM System, two sets of real DNA sequences from both sample human (noncoding DNA) and corn (coding DNA) genomes are collected in comparison with pseudo DNA sequences generated by HC-256 to show their intrinsic properties in higher levels of similar relationships among relevant DNA sequences on 2D maps. Sample 2D maps are listed and their characteristics are illustrated under controllable environment. Visual results are briefly analyzed to explore their intrinsic properties on selected genome sequences.

Investigation of the stable and the metastable liquidus miscibility gaps in Fe–Sn and Fe–Cu binary systems

Two kinds of experimental methods were tried in the present work:(i)the powder metallurgy method combined with differential thermal analysis(DTA)to determine the metastable liquidus miscibility gap for a Fe–Cu binary system and(ii)the high-temperature melting method combined with isothermal treatment to determine the stable liquidus miscibility gap for a Fe–Sn binary system.The experimental method was adopted according to the characteristics of the liquidus miscibility gap of the specific system.Using the powder metallurgy method,a uniform microstructure morphology and chemical composition was obtained in the DTA specimen,and the phase-separation temperature of the supercooled metastable liquid was measured.The isothermal treatment was applied for the samples inside the stable liquidus miscibility gap;here,equilibrated compositions were reached,and a layered morphology was formed after rapid cooling.The liquid miscibility gaps of the Fe–Cu and Fe–Sn binary systems were measured,and the peak temperatures of the corresponding miscibility gaps were determined to be about 1417°C at x(Cu)=0.465 at%and 1350°C at x(Sn)=0.487 at%,respectively.On the basis of the experimental results,both the Fe–Cu and the Fe–Sn binary systems were thermodynamically assessed.

Comparative Study of Different Decoupling Schemes for TITO Binary Distillation Column via PI Controller

This paper presents a comparative study of different decoupling control schemes for a two-input, two-output(TITO) binary distillation column via proportional-integral(PI)controller. The key idea behind this paper is designing two novel fuzzy decoupling schemes that depend on human knowledge,instead of the system mathematical model used in conventional decoupling schemes. Based on conventional and inverted decoupling schemes, fuzzy and inverted fuzzy decoupling schemes are developed. The control effect is compared using simulation results for the proposed two schemes with conventional decoupling and inverted decoupling. The proposed fuzzy decoupling schemes are easy to realize and simple to design, besides they have a good decoupling capability. Two methods are used to prove asymptotic stability of each loop and the entire closed-loop system by applying the proposed fuzzy decoupling-based PI controller.The Wood and Berry model of a binary distillation column is used to illustrate the applicability of the proposed schemes.

Measuring the orbital periods of low mass X-ray binaries in the X-ray band

A low mass X-ray binary （LMXB） contains either a neutron star or a black hole accreting materials from its low mass companion star. It is one of the primary astrophysical sources for studying stellar-mass compact objects and accreting phe- nomena. As with other binary systems, the most important parameter of an LMXB is the orbital period, which allows us to learn about the nature of the binary system and constrain the properties of the system＇s components, including the compact ob- ject. As a result, measuring the orbital periods of LMXBs is essential for investigating these systems even though fewer than half of them have known orbital periods. This article introduces the different methods for measuring the orbital periods in the X-ray band and reviews their application to various types of LMXBs, such as eclipsing and dipping sources, as well as pulsar LMXBs.

Period Correction and Evolutionary Status of the MACHO Eclipsing Binary System 47.2135.29

The eclipsing binary system which is catalogued in MACHO as 47.2135.29 is analyzed in detail for orbital elements and absolute parameters based on the MACHO observations and the latest values for the distance modulus of LMC. The period of the system is found more accurately, by minimizing the dispersion in the folded light curves. The binary is found to be a young semi-detached binary and is potentially a beta Lyrae system.

Classification of Gastric Lesions Using Gabor Block Local Binary Patterns

The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems.This generic nature demands the image descriptors to be invariant to illumination gradients,scaling,homogeneous illumination,and rotation.In this article,we devise a novel feature extraction methodology,which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors.We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation,scale and illumination invariant features.The invariance characteristics of the proposed Gabor Block Local Binary Patterns(GBLBP)are demonstrated using a publicly available texture dataset.We use the proposed feature extraction methodology to extract texture features from Chromoendoscopy(CH)images for the classification of cancer lesions.The proposed feature set is later used in conjuncture with convolutional neural networks to classify the CH images.The proposed convolutional neural network is a shallow network comprising of fewer parameters in contrast to other state-of-the-art networks exhibiting millions of parameters required for effective training.The obtained results reveal that the proposed GBLBP performs favorably to several other state-of-the-art methods including both hand crafted and convolutional neural networks-based features.