Detecting plagiarized mobile apps using API birthmarks

This paper addresses the problem of detecting plagiarized mobile apps. Plagiarism is the practice of building mobile apps by reusing code from other apps without the consent of the corresponding app developers. Recent studies on third-party app markets have suggested that plagiarized apps are an important vehicle for malware delivery on mobile phones. Malware authors repackage official versions of apps with malicious functionality, and distribute them for free via these third-party app markets. An effective technique to detect app plagiarism can therefore help identify malicious apps. Code plagiarism has long been a problem and a number of code similarity detectors have been developed over the years to detect plagiarism. In this paper we show that obfuscation techniques can be used to easily defeat similarity detectors that rely solely on statically scanning the code of an app. We propose a dynamic technique to detect plagiarized apps that works by observing the interaction of an app with the underlying mobile platform via its API invocations. We propose API birthmarks to characterize unique app behaviors, and develop a robust plagiarism detection tool using API birthmarks.     

Online handwriting recognition: the NPen++ recognizer

This paper presents the online handwriting recognition system NPen++ developed at the University of Karlsruhe and Carnegie Mellon University. The NPen++ recognition engine is based on a multi-state time delay neural network and yields recognition rates from 96% for a 5,000 word dictionary to 93.4% on a 20,000 word dictionary and 91.2% for a 50,000 word dictionary. The proposed tree search and pruning technique reduces the search space considerably without losing too much recognition performance compared to an exhaustive search. This enables the NPen++ recognizer to be run in real-time with large dictionaries. Initial recognition rates for whole sentences are promising and show that the MS-TDNN architecture is suited to recognizing handwritten data ranging from single characters to whole sentences. Received September 3, 2000 / Revised October 9, 2000     

An entropic characterization of protein interaction networks and cellular robustness.

The structure of molecular networks is believed to determine important aspects of their cellular function, such as the organismal resilience against random perturbations. Ultimately, however, cellular behaviour is determined by the dynamical processes, which are constrained by network topology. The present work is based on a fundamental relation from dynamical systems theory, which states that the macroscopic resilience of a steady state is correlated with the uncertainty in the underlying microscopic processes, a property that can be measured by entropy. Here, we use recent network data from large-scale protein interaction screens to characterize the diversity of possible pathways in terms of network entropy. This measure has its origin in statistical mechanics and amounts to a global characterization of both structural and dynamical resilience in terms of microscopic elements. We demonstrate how this approach can be used to rank network elements according to their contribution to network entropy and also investigate how this suggested ranking reflects on the functional data provided by gene knockouts and RNAi experiments in yeast and Caenorhabditis elegans. Our analysis shows that knockouts of proteins with large contribution to network entropy are preferentially lethal. This observation is robust with respect to several possible errors and biases in the experimental data. It underscores the significance of entropy as a fundamental invariant of the dynamical system, and as a measure of structural and dynamical properties of networks. Our analytical approach goes beyond the phenomenological studies of cellular robustness based on local network observables, such as connectivity. One of its principal achievements is to provide a rationale to study proxies of cellular resilience and rank proteins according to their importance within the global network context.     

FPGA Implementation of Cross Virtual Concatenation Transmitter/ Receiver for Data Transmission over Next Generation SDH Systems

Cross Virtual Concatenation is the new technique proposed for bandwidth efficient transmission of data over SDH networks. SDH networks came into existence for reliable voice transmission. As the demand of data traffic grew in wide area networks, new technologies were developed and standardized for data transmission over SDH networks. The technologies used namely, GFP (generic framing procedure), VCAT (virtual concatenation) and LCAS (link capacity adjustment scheme) enable network operator to provide integrated voice and data services over their legacy SDH infrastructure. Data packets are encapsulated using framing protocols GFP. VCAT is a process of distributing the GFP framed data payload in number of virtual channels of same capacity forming a Virtually Concatenated Group (VCG). LCAS is used for dynamic bandwidth allocation. LCAS enhances the VCAT scheme with hitless in service addition and removal of VC’s to/from the VCG.VCAT combines homogeneous virtual channels together which in some cases limits the performance of VCAT. This paper describes the implementation of new concatenation technology named cross virtual concatenation (CVC), which combines heterogeneous VC’s together to utilize the SDH bandwidth more efficiently. CVC implementation requires only end node equipments to be upgraded as VCG members travel through the link similar to the conventional VCAT. This paper proposes FPGA implementation of transmitter and receiver circuits for 100 Mbps Ethernet data transmission over next Generation SDH systems using CVC, where two types of VC’s namely VC-3 and VC-12 are used for data transmission. Total Transmission delay is calculated as 125 μs. There is no complexity added at the receiver side due to this delay. The receiver is designed for 32 ms differential delay compensation which can be increased up to maximum 256 ms by increasing the buffer size at the receiver.     

Effect of Fiber Length on Carbon Nanotube-Induced Fibrogenesis

Given their extremely small size and light weight, carbon nanotubes (CNTs) can be readily inhaled by human lungs resulting in increased rates of pulmonary disorders, particularly fibrosis. Although the fibrogenic potential of CNTs is well established, there is a lack of consensus regarding the contribution of physicochemical attributes of CNTs on the underlying fibrotic outcome. We designed an experimentally validated in vitro fibroblast culture model aimed at investigating the effect of fiber length on single-walled CNT (SWCNT)-induced pulmonary fibrosis. The fibrogenic response to short and long SWCNTs was assessed via oxidative stress generation, collagen expression and transforming growth factor-beta (TGF-β) production as potential fibrosis biomarkers. Long SWCNTs were significantly more potent than short SWCNTs in terms of reactive oxygen species (ROS) response, collagen production and TGF-β release. Furthermore, our finding on the length-dependent in vitro fibrogenic response was validated by the in vivo lung fibrosis outcome, thus supporting the predictive value of the in vitro model. Our results also demonstrated the key role of ROS in SWCNT-induced collagen expression and TGF-β activation, indicating the potential mechanisms of length-dependent SWCNT-induced fibrosis. Together, our study provides new evidence for the role of fiber length in SWCNT-induced lung fibrosis and offers a rapid cell-based assay for fibrogenicity testing of nanomaterials with the ability to predict pulmonary fibrogenic response in vivo.     

Advances in neutron imaging

Imaging techniques based on neutron beams are rapidly developing and have become versatile non-destructive analyzing tools in many research fields. Due to their intrinsic properties, neutrons differ strongly from electrons, protons or X-rays in terms of their interaction with matter: they penetrate deeply into most common metallic materials while they have a high sensitivity to light elements such as hydrogen, hydrogenous substances, or lithium. This makes neutrons perfectly suited probes for research on materials that are used for energy storage and conversion, e.g., batteries, hydrogen storage, fuel cells, etc. Moreover, their wave properties can be exploited to perform diffraction, phase-contrast and dark-field imaging experiments. Their magnetic moment allows for resolving magnetic properties in bulk samples. This review will focus on recent applications of neutron imaging techniques in both materials research and fundamental science illustrated by examples selected from different areas.     

Electrohydrodynamic atomization (EHDA) of high-conductivity pure solvent

ABSTRACT

In this study, the effects of flow rate, liquid conductivity and neutralizer strength on the operating and output characteristics are examined for an Electrohydrodynamic atomization (EHDA)-based aerosol generator system, designed and built in our laboratory. Ethylene glycol is used for generating the droplets. It is seen that stable cone-jet mode could be obtained for solvent conductivity as high as 240 μS/cm. While the output number concentration was found to increase with both flow rate and conductivity, the mean particle diameters decreased with increasing conductivity and increased with flow rate. The mean droplet size and output number concentration are found to be about three times more sensitive to changes in conductivity as compared to that of flow rate in the range of 26–150 μS/cm. It is also observed that throughput from the system is strongly dependent on the strength of the charge neutralizer. In the present setup, 8 μCi ²⁴¹Am bipolar neutralizer in axisymmetric configuration was found to be sufficient to neutralize the generated droplets.     

Computational design of thermoset nanocomposite coatings: Methodological study on coating development and testing

Thermoset nanocomposites (TSNCs) may offer significantly improved performance over conventional thermoset materials, and thus are attractive for wide industrial applications, especially in the coating industry. Design of TSNCs via experiment, however, faces various technical challenges due to design complexity. Computational design can provide deep insights and identify superior design solutions through exploring opportunities in a usually huge design space. This paper introduces a generic computational methodology for the design, characterization, and testing of TSNC-based coatings. A distinct feature of the methodology is its capability of generating quantitative correlations among material formulation, processing condition, coating microstructure and property, coating performance, and processing efficiency. The correlations can enable a comprehensive analysis for optimal TSNC coating design. Case studies will demonstrate the methodological efficacy and attractiveness.     

Advanced neutron imaging methods with a potential to benefit from pulsed sources

During the last decade neutron imaging has seen significant improvements in instrumentation, detection and spatial resolution. Additionally, a variety of new applications and methods have been explored. As a consequence of an outstanding development nowadays various techniques of neutron imaging go far beyond a two- and three-dimensional mapping of the attenuation coefficients for a broad range of samples. Neutron imaging has become sensitive to neutron scattering in the small angle scattering range as well as with respect to Bragg scattering. Corresponding methods potentially provide spatially resolved and volumetric data revealing microstructural inhomogeneities, texture variations, crystalline phase distributions and even strains in bulk samples. Other techniques allow for the detection of refractive index distribution through phase sensitive measurements and the utilization of polarized neutrons enables radiographic and tomographic investigations of magnetic fields and properties as well as electrical currents within massive samples. All these advanced methods utilize or depend on wavelength dependent signals, and are hence suited to profit significantly from pulsed neutron sources as will be discussed.