A New Android Malware Detection Method Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.

Spitzer infrared spectrograph point source classification in the Small Magellanic Cloud

The Magellanic Clouds are uniquely placed to study the stellar contribution to dust emission. Individual stars can be resolved in these systems even in the mid-infrared, and they are close enough to allow detection of infrared excess caused by dust. We have searched the Spitzer Space Telescope data archive for all Infrared Spectrograph (IRS) staring-mode observations of the Small Magellanic Cloud (SMC) and found that 209 Infrared Array Camera (IRAC) point sources within the footprint of the Surveying the Agents of Galaxy Evolution in the Small Magellanic Cloud (SAGE-SMC) Spitzer Legacy programme were targeted, within a total of 311 staring-mode observations. We classify these point sources using a decision tree method of object classification, based on infrared spectral features, continuum and spectral energy distribution shape, bolometric luminosity, cluster membership and variability information. We find 58 asymptotic giant branch (AGB) stars, 51 young stellar objects, 4 post-AGB objects, 22 red supergiants, 27 stars (of which 23 are dusty OB stars), 24 planetary nebulae (PNe), 10 Wolf-Rayet stars, 3 H II regions, 3 R Coronae Borealis stars, 1 Blue Supergiant and 6 other objects, including 2 foreground AGB stars. We use these classifications to evaluate the success of photometric classification methods reported in the literature.

A Eulogy to Seed Sharing: Manufacturing Genuine Scarcity

Seeds are traditionally considered as common or even public goods, their traits as ‘products of nature’. They are also essential to biodiversity, food security and food sovereignty. However, a suite of techno-legal interventions has legislated the enclosure of seeds: seed patents, plant variety protections, and stewardship agreements. These instruments create and protect private proprietary interests over plant material and point to the interface between seeds, capitalism, and law. In the following article, we consider the latest innovations, the bulk of which have been directed toward genetically disabling the reproductive capacities of seeds (terminator technology) or tying these capacities to outputs (‘round-up necessary’). In both instances, scarcity moves from artificial to real.
For the agro-industrial complex, the innovations are perfectly rational as they can simultaneously control supply and demand. For those outside the complex, however, the consequences are potentially ruinous. The practices of seed-saving and exchange no longer are feasible, even covertly. Contemporary genetic controls have upped the ante, by either disabling the reproductive capacity of seeds or, through cross-pollination and outcrossing, facilitating the autonomous spread of the genetic modifications that are importantly still traceable, identifiable and therefore capable of legal protection. In both instances, genuine scarcity becomes the new standard as private interests dominate what was a public sphere.

Seed dormancy in Arabidopsis thaliana is controlled by alternative polyadenylation of DOG1

DOG1 is a key regulator of seed dormancy in Arabidopsis and other plants. Interestingly, the C-terminus of DOG1 is either absent or not conserved in many plant species. Here, we show that in Arabidopsis DOG1 transcript is subject to alternative polyadenylation. In line with this, mutants in RNA 3' processing complex display weakened seed dormancy in parallel with defects in DOG1 proximal polyadenylation site selection, suggesting that the short DOG1 transcript, is functional. This is corroborated by the finding that the proximally polyadenylated short DOG1 mRNA is translated in vivo and complements the dog1 mutation. In summary, our findings indicate that the short DOG1 protein isoform produced from the proximally polyadenylated DOG1 mRNA is a key player in the establishment of seed dormancy in Arabidopsis and characterize a set of mutants in RNA 3' processing complex required for production of proximally polyadenylated functional DOG1 transcript.

Comparison of granulometric methods and sampling strategies used in marine habitat classification and Ecological Status assessment

Sediment particle size analysis (PSA) is routinely used to support benthic macrofaunal community distribution data in habitat mapping and Ecological Status (ES) assessment. No optimal PSA Method to explain variability in multivariate macrofaunal distribution has been identified nor have the effects of changing sampling strategy been examined. Here, we use benthic macrofaunal and PSA grabs from two embayments in the south of Ireland. Four frequently used PSA Methods and two common sampling strategies are applied. A combination of laser particle sizing and wet/dry sieving without peroxide pre-treatment to remove organics was identified as the optimal Method for explaining macrofaunal distributions. ES classifications and EUNIS sediment classification were robust to changes in PSA Method. Fauna and PSA samples returned from the same grab sample significantly decreased macrofaunal variance explained by PSA and caused ES to be classified as lower. Employing the optimal PSA Method and sampling strategy will improve benthic monitoring. © 2012 Elsevier Ltd.

Molecular classification of non-invasive breast lesions for personalised therapy and chemoprevention

Breast cancer screening has led to a dramatic increase in the detection of pre-invasive breast lesions. While mastectomy is almost guaranteed to treat the disease, more conservative approaches could be as effective if patients can be stratified based on risk of co-existing or recurrent invasive disease.Here we use a range of biomarkers to interrogate and classify purely non-invasive lesions (PNL) and those with co-existing invasive breast cancer (CEIN). Apart from Ductal Carcinoma In Situ (DCIS), relative homogeneity is observed. DCIS contained a greater spread of molecular subtypes. Interestingly, high expression of p-mTOR was observed in all PNL with lower expression in DCIS and invasive carcinoma while the opposite expression pattern was observed for TOP2A.Comparing PNL with CEIN, we have identified p53 and Ki67 as predictors of CEIN with a combined PPV and NPV of 90.48% and 43.3% respectively. Furthermore, HER2 expression showed the best concordance between DCIS and its invasive counterpart.We propose that these biomarkers can be used to improve the management of patients with pre-invasive breast lesions following further validation and clinical trials. p53 and Ki67 could be used to stratify patients into low and high-risk groups for co-existing disease. Knowledge of expression of more actionable targets such as HER2 or TOP2A can be used to design chemoprevention or neo-adjuvant strategies. Increased knowledge of the molecular profile of pre-invasive lesions can only serve to enhance our understanding of the disease and, in the era of personalised medicine, bring us closer to improving breast cancer care.

Kernelized structural classification for 3D dogs body parts detection

Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the welfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition, we propose a system for recognizing body parts of dogs kept in pens. We decide to adopt both 2D and 3D features in order to obtain a rich description of the dog model. Images are acquired using the Microsoft Kinect to capture the depth map images of the dog. Upon depth maps a Structural Support Vector Machine (SSVM) is employed to identify the body parts using both 3D features and 2D images. The proposal relies on a kernelized discriminative structural classificator specifically tailored for dogs independently from the size and breed. The classification is performed in an online fashion using the LaRank optimization technique to obtaining real time performances. Promising results have emerged during the experimental evaluation carried out at a dog shelter, managed by IZSAM, in Teramo, Italy.

A Configurable Packet Classification Architecture for Software-Defined Networking

Network management tools must be able to monitor and analyze traffic flowing through network systems. According to the OpenFlow protocol applied in Software-Defined Networking (SDN), packets are classified into flows that are searched in flow tables. Further actions, such as packet forwarding, modification, and redirection to a group table, are made in the flow table with respect to the search results. A novel hardware solution for SDN-enabled packet classification is presented in this paper. The proposed scheme is focused on a label-based search method, achieving high flexibility in memory usage. The implemented hardware architecture provides optimal lookup performance by configuring the search algorithm and by performing fast incremental update as programmed the software controller.