Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

PLUMED 2: New feathers for an old bird

Enhancing sampling and analyzing simulations are central issues in molecular simulation. Recently, we introduced PLUMED, an open-source plug-in that provides some of the most popular molecular dynamics (MD) codes with implementations of a variety of different enhanced sampling algorithms and collective variables (CVs). The rapid changes in this field, in particular new directions in enhanced sampling and dimensionality reduction together with new hardware, require a code that is more flexible and more efficient. We therefore present PLUMED 2 here a,complete rewrite of the code in an object-oriented programming language (C++). This new version introduces greater flexibility and greater modularity, which both extends its core capabilities and makes it far easier to add new methods and CVs. It also has a simpler interface with the MD engines and provides a single software library containing both tools and core facilities. Ultimately, the new code better serves the ever-growing community of users and contributors in coping with the new challenges arising in the field.

Program summary

Program title: PLUMED 2

Catalogue identifier: AEEE_v2_0

Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEEE_v2_0.html

Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland

Licensing provisions: Yes

No. of lines in distributed program, including test data, etc.: 700646

No. of bytes in distributed program, including test data, etc.: 6618136

Distribution format: tar.gz

Programming language: ANSI-C++.

Computer: Any computer capable of running an executable produced by a C++ compiler.

Operating system: Linux operating system, Unix OSs.

Has the code been vectorized or parallelized?: Yes, parallelized using MPI.

RAM: Depends on the number of atoms, the method chosen and the collective variables used.

Classification: 3, 7.7, 23. Catalogue identifier of previous version: AEEE_v1_0.

Journal reference of previous version: Comput. Phys. Comm. 180 (2009) 1961.

External routines: GNU libmatheval, Lapack, Bias, MPI. (C) 2013 Elsevier B.V. All rights reserved.

Novel genetic susceptibility loci for diabetic end-stage renal disease identified through robust naive Bayes classification

Aims/hypothesis: Diabetic nephropathy is a major diabetic complication, and diabetes is the leading cause of end-stage renal disease (ESRD). Family studies suggest a hereditary component for diabetic nephropathy. However, only a few genes have been associated with diabetic nephropathy or ESRD in diabetic patients. Our aim was to detect novel genetic variants associated with diabetic nephropathy and ESRD. Methods: We exploited a novel algorithm, ‘Bag of Naive Bayes’, whose marker selection strategy is complementary to that of conventional genome-wide association models based on univariate association tests. The analysis was performed on a genome-wide association study of 3,464 patients with type 1 diabetes from the Finnish Diabetic Nephropathy (FinnDiane) Study and subsequently replicated with 4,263 type 1 diabetes patients from the Steno Diabetes Centre, the All Ireland-Warren 3-Genetics of Kidneys in Diabetes UK collection (UK–Republic of Ireland) and the Genetics of Kidneys in Diabetes US Study (GoKinD US). Results: Five genetic loci (WNT4/ZBTB40-rs12137135, RGMA/MCTP2-rs17709344, MAPRE1P2-rs1670754, SEMA6D/SLC24A5-rs12917114 and SIK1-rs2838302) were associated with ESRD in the FinnDiane study. An association between ESRD and rs17709344, tagging the previously identified rs12437854 and located between the RGMA and MCTP2 genes, was replicated in independent case–control cohorts. rs12917114 near SEMA6D was associated with ESRD in the replication cohorts under the genotypic model (p < 0.05), and rs12137135 upstream of WNT4 was associated with ESRD in Steno. Conclusions/interpretation: This study supports the previously identified findings on the RGMA/MCTP2 region and suggests novel susceptibility loci for ESRD. This highlights the importance of applying complementary statistical methods to detect novel genetic variants in diabetic nephropathy and, in general, in complex diseases.

Physical and dynamical characterisation of low ΔV NEA (190491) 2000 FJ10

Aims: We investigated the physical properties and dynamical evolution of near-Earth asteroid (NEA) (190491) 2000 FJ10 in order to assess the suitability of this accessible NEA as a space mission target. Methods: Photometry and colour determination were carried out with the 1.54 m Kuiper Telescope (Mt Bigelow, USA) and the 10 m Southern African Large Telescope (SALT; Sutherland, South Africa) during the object's recent favourable apparition in 2011-12. During the earlier 2008 apparition, a spectrum of the object in the 6000-9000 Angstrom region was obtained with the 4.2 m William Herschel Telescope (WHT; Canary Islands, Spain). Interpretation of the observational results was aided by numerical simulations of 1000 dynamical clones of 2000 FJ10 up to 106 yr in the past and in the future. Results: The asteroid's spectrum and colours determined by our observations suggest a taxonomic classification within the S-complex although other classifications (V, D, E, M, P) cannot be ruled out. On this evidence, it is unlikely to be a primitive, relatively unaltered remnant from the early history of the solar system and thus a low priority target for robotic sample return. Our photometry placed a lower bound of 2 h to the asteroid's rotation period. Its absolute magnitude was estimated to be 21.54 ± 0.1 which, for a typical S-complex albedo, translates into a diameter of 130 ± 20 m. Our dynamical simulations show that it has likely been an Amor for the past 105 yr. Although currently not Earth-crossing, it will likely become so during the period 50-100 kyr in the future. It may have arrived from the inner or central main belt >1 Myr ago as a former member of a low-inclination S-class asteroid family. Its relatively slow rotation and large size make it a suitable destination for a human mission. We show that ballistic Earth-190491-Earth transfer trajectories with ΔV <2 km s-1 at the asteroid exist between 2052 and 2061. Based on observations made with the Southern African Large Telescope (SALT).

The Pan-STARRS Moving Object Processing System

We describe the Pan-STARRS Moving Object Processing System (MOPS), a modern software package that produces automatic asteroid discoveries and identifications from catalogs of transient detections from next-generation astronomical survey telescopes. MOPS achieves >99.5% efficiency in producing orbits from a synthetic but realistic population of asteroids whose measurements were simulated for a Pan-STARRS4-class telescope. Additionally, using a nonphysical grid population, we demonstrate that MOPS can detect populations of currently unknown objects such as interstellar asteroids. MOPS has been adapted successfully to the prototype Pan-STARRS1 telescope despite differences in expected false detection rates, fill-factor loss, and relatively sparse observing cadence compared to a hypothetical Pan-STARRS4 telescope and survey. MOPS remains highly efficient at detecting objects but drops to 80% efficiency at producing orbits. This loss is primarily due to configurable MOPS processing limits that are not yet tuned for the Pan-STARRS1 mission. The core MOPS software package is the product of more than 15 person-years of software development and incorporates countless additional years of effort in third-party software to perform lower-level functions such as spatial searching or orbit determination. We describe the high-level design of MOPS and essential subcomponents, the suitability of MOPS for other survey programs, and suggest a road map for future MOPS development.

Object oriented supply chain simulation in Flexsim

12. Vlajic, J., Bjelic. N., Vidovic, M., (2006), “Object oriented supply chain simulation in Flexsim”, Proceedings of The microCAD 2006 International Scientific Conference, Miskolc, Hungary, pp.197-203;

Tunnel spoil classification and applicability of lime addition in weak formations for muck reuse

Tunnel construction planning requires careful consideration of the spoil management part, as this involves environmental, economic and legal requirements. In this paper a methodological approach that considers the interaction between technical and geological factors in determining the features of the resulting muck is proposed. This gives indications about the required treatments as well as laboratory and field characterisation tests to be performed to assess muck recovery alternatives. While this reuse is an opportunity for excavations in good quality homogeneous grounds (e.g. granitic mass), it is critical for complex formation. This approach has been validated, at present, for three different geo-materials resulting from a tunnel excavation carried out with a large diameter Earth Pressure Balance Shield (EPB) through a complex geological succession. Physical parameters and technological features of the three materials have been assessed, according to their valorisation potential, for defining re-utilisation patterns. The methodology proved to be effective and the laboratory tests carried out on the three materials allowed the suitability and treatment effectiveness for each muck recovery strategy to be defined. © 2014 Elsevier Ltd.

The distribution of COPD in UK general practice using the new GOLD classification

The new Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2011 document recommends a combined assessment of chronic obstructive pulmonary disease (COPD) based on current symptoms and future risk.

A large database of primary-care COPD patients across the UK was used to determine COPD distribution and characteristics according to the new GOLD classification. 80 general practices provided patients with a Read code diagnosis of COPD. Electronic and hand searches of patient medical records were undertaken, optimising data capture.

Data for 9219 COPD patients were collected. For the 6283 patients with both forced expiratory volume in 1 s (FEV1) and modified Medical Research Council scores (mean¡SD age 69.2¡10.6 years, body mass index 27.3¡6.2 kg?m-2), GOLD 2011 group distributions were: A (low risk and fewer symptoms) 36.1%, B (low risk and more symptoms) 19.1%, C (high risk and fewer symptoms) 19.6% and D (high risk and more symptoms) 25.3%. This is in contrast with GOLD 2007 stage classification: I (mild) 17.1%, II (moderate) 52.2%, III (severe) 25.5% and IV (very severe) 5.2%. 20% of patients with FEV1 o50% predicted had more than two exacerbations in the previous 12 months. 70% of patients with FEV1 ,50% pred had fewer than two exacerbations in the previous 12 months.

This database, representative of UK primary-care COPD patients, identified greater proportions of patients in the mildest and most severe categories upon comparing 2011 versus 2007 GOLD classifications. Discordance between airflow limitation severity and exacerbation risk was observed.

Prediction of integral type failures in semiconductor manufacturing through classification methods

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.