Android Malware Clustering through Malicious Payload Mining

Clustering has been well studied for desktop malware analysis as an effective triage method. Conventional similarity-based clustering techniques, however, cannot be immediately applied to Android malware analysis due to the excessive use of third-party libraries in Android application development and the widespread use of repackaging in malware development. We design and implement an Android malware clustering system through iterative mining of malicious payload and checking whether malware samples share the same version of malicious payload. Our system utilizes a hierarchical clustering technique and an efficient bit-vector format to represent Android apps. Experimental results demonstrate that our clustering approach achieves precision of 0.90 and recall of 0.75 for Android Genome malware dataset, and average precision of 0.98 and recall of 0.96 with respect to manually verified ground-truth.Comment: Proceedings of the 20th International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2017

Swelling From Down Under... The Patella

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All the World's a (Hyper)Graph: A Data Drama

We introduce Hyperbard, a dataset of diverse relational data representationsderived from Shakespeare's plays. Our representations range from simple graphscapturing character co-occurrence in single scenes to hypergraphs encodingcomplex communication settings and character contributions as hyperedges withedge-specific node weights. By making multiple intuitive representationsreadily available for experimentation, we facilitate rigorous representationrobustness checks in graph learning, graph mining, and network analysis,highlighting the advantages and drawbacks of specific representations.Leveraging the data released in Hyperbard, we demonstrate that many solutionsto popular graph mining problems are highly dependent on the representationchoice, thus calling current graph curation practices into question. As anhomage to our data source, and asserting that science can also be art, wepresent all our points in the form of a play.<br

Possible natural cytoplasmic variants of N. intermedia.

Possible natural cytoplasmic variants of N. intermedia

Nesting, spin-fluctuations, and odd-gap superconductivity in NaxCoO2 yH2O

We have calculated the one-electron susceptibility of hydrated NaxCoO2 and find strong nesting nearly commensurate with a 2X2 superstructure. The nesting involves about 70% of all electrons at the Fermi level and is robust with respect to doping. This nesting creates a tendency to a charge density wave compatible with the charge order often seen at x approx 0.5, which is usually ascribed to electrostatic repulsion of Na ions. In the spin channel, it gives rise to strong spin-fluctuations, which should be important for superconductivity. The superconducting state most compatible with this nesting structure is an odd-gap triplet s-wave state.Comment: 4 figure

Relationship of Weed Control and Soil pH to No-Tillage Corn Yields

Atrazine and simazine are used for selective control of a broad spectrum of weeds in corn. Over 80% of the U.S. corn production is treated with one or the other of these two s-triazine herbicides. In Kentucky they are used annually on over 800,000 acres of corn, including over 200,000 acres of no-tillage corn. When added to the soil these compounds are ultimately degraded to non-phytotoxic compounds. The rate of degradation is dependent upon the physical, chemical, and biological properties of the soil. Although atrazine and simazine are chemically similar, simazine is considered to degrade slightly slower than atrazine after application to the soil and as a result will remain in the soil for a longer period of time

Classifying Annihilating-Ideal Graphs of Commutative Artinian Rings

In this article we investigate the annihilating-ideal graph of a commutative ring, introduced by Behboodi and Rakeei in [BR11a]. Our main goal is to determine which algebraic properties of a ring are reflected in its annihilating-ideal graph. We prove that, for artinian rings, the annihilating-ideal graph can be used to determine whether the ring in question is a PIR or, more generally, if it is a dual ring. Moreover, with one trivial exception, the annihilating-ideal graph can distinguish between PIRs with different ideal lattices. In addition, we explore new techniques for classifying small annihilating-ideal graphs. Consequently, we completely determine the graphs with 6 or fewer vertices which can be realized as the annihilating-ideal graph of a commutative ring

A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images

Diffusion models are a special type of generative model, capable of synthesising new data from a learnt distribution. We introduce DISPR, a diffusion-based model for solving the inverse problem of three-dimensional (3D) cell shape prediction from two-dimensional (2D) single cell microscopy images. Using the 2D microscopy image as a prior, DISPR is conditioned to predict realistic 3D shape reconstructions. To showcase the applicability of DISPR as a data augmentation tool in a feature-based single cell classification task, we extract morphological features from the red blood cells grouped into six highly imbalanced classes. Adding features from the DISPR predictions to the three minority classes improved the macro F1 score from $F1_\text{macro} = 55.2 \pm 4.6\%$ to $F1_\text{macro} = 72.2 \pm 4.9\%$. We thus demonstrate that diffusion models can be successfully applied to inverse biomedical problems, and that they learn to reconstruct 3D shapes with realistic morphological features from 2D microscopy images