User's guide to the Reliability Estimation System Testbed (REST)

The Reliability Estimation System Testbed is an X-window based reliability modeling tool that was created to explore the use of the Reliability Modeling Language (RML). RML was defined to support several reliability analysis techniques including modularization, graphical representation, Failure Mode Effects Simulation (FMES), and parallel processing. These techniques are most useful in modeling large systems. Using modularization, an analyst can create reliability models for individual system components. The modules can be tested separately and then combined to compute the total system reliability. Because a one-to-one relationship can be established between system components and the reliability modules, a graphical user interface may be used to describe the system model. RML was designed to permit message passing between modules. This feature enables reliability modeling based on a run time simulation of the system wide effects of a component's failure modes. The use of failure modes effects simulation enhances the analyst's ability to correctly express system behavior when using the modularization approach to reliability modeling. To alleviate the computation bottleneck often found in large reliability models, REST was designed to take advantage of parallel processing on hypercube processors

Gerbil: A Fast and Memory-Efficient kk-mer Counter with GPU-Support

A basic task in bioinformatics is the counting of $k$-mers in genome strings. The $k$-mer counting problem is to build a histogram of all substrings of length $k$ in a given genome sequence. We present the open source $k$-mer counting software Gerbil that has been designed for the efficient counting of $k$-mers for $k\geq32$. Given the technology trend towards long reads of next-generation sequencers, support for large $k$ becomes increasingly important. While existing $k$-mer counting tools suffer from excessive memory resource consumption or degrading performance for large $k$, Gerbil is able to efficiently support large $k$ without much loss of performance. Our software implements a two-disk approach. In the first step, DNA reads are loaded from disk and distributed to temporary files that are stored at a working disk. In a second step, the temporary files are read again, split into $k$-mers and counted via a hash table approach. In addition, Gerbil can optionally use GPUs to accelerate the counting step. For large $k$, we outperform state-of-the-art open source $k$-mer counting tools for large genome data sets.Comment: A short version of this paper will appear in the proceedings of WABI 201

State Space Methods in Stata

We illustrate how to estimate parameters of linear state-space models using the Stata program sspace. We provide examples of how to use sspace to estimate the parameters of unobserved-component models, vector autoregressive moving-average models, and dynamic-factor models. We also show how to compute one-step, filtered, and smoothed estimates of the series and the states; dynamic forecasts and their confidence intervals; and residuals.

UNICORE COMMANDLINE CLIENT: USER MANUAL

The UNICORE Commandline client (UCC) is a full-featured client for the UNICORE Grid middlewar

FASTAFS:file system virtualisation of random access compressed FASTA files

Background: The FASTA file format, used to store polymeric sequence data, has become a bioinformatics file standard used for decades. The relatively large files require additional files, beyond the scope of the original format, to identify sequences and to provide random access. Multiple compressors have been developed to archive FASTA files back and forth, but these lack direct access to targeted content or metadata of the archive. Moreover, these solutions are not directly backwards compatible to FASTA files, resulting in limited software integration. Results: We designed a linux based toolkit that virtualises the content of DNA, RNA and protein FASTA archives into the filesystem by using filesystem in userspace. This guarantees in-sync virtualised metadata files and offers fast random-access decompression using bit encodings plus Zstandard (zstd). The toolkit, FASTAFS, can track all its system-wide running instances, allows file integrity verification and can provide, instantly, scriptable access to sequence files and is easy to use and deploy. The file compression ratios were comparable but not superior to other state of the art archival tools, despite the innovative random access feature implemented in FASTAFS. Conclusions: FASTAFS is a user-friendly and easy to deploy backwards compatible generic purpose solution to store and access compressed FASTA files, since it offers file system access to FASTA files as well as in-sync metadata files through file virtualisation. Using virtual filesystems as in-between layer offers format conversion without the need to rewrite code into different programming languages while preserving compatibility.</p

Findings of a comparison of five filing protocols

Filing protocols are essential for the management and dissemination of shared information within computer systems. This is a survey of the current state of the art in filing protocols. Five popular filing protocols were selected and subjected to a rigorous comparison. FTAM, FTP, UNIX rep, XNS Filing, and NFS are compared in the following areas: exported interface, concurrency control, access control, error recovery, and performance. The coverage of background material includes a taxonomy and a brief history of filing protocols

Amun : a python honeypot

In this report we describe a low-interaction honeypot, which is capable of capturing autonomous spreading malware from the internet, named Amun. For this purpose, the software emulates a wide range of different vulnerabilities. As soon as an attacker exploits one of the emulated vulnerabilities the payload transmitted by the attacker is analyzed and any download URL found is extracted. Next, the honeypot tries to download the malicious software and store it on the local harddisc, for further analyses. As a result, we are able to collect at best unknown binaries of malware that automatically spreads across the network. The collected samples can for example be used to help anti-virus vendors improve their signatures

University Information Technology Services' Advanced IT Facilities: The least every researcher needs to know

This is an archived document containing instructions for using IU's advanced IT facilities ca. 2003. A version of this document updated in 2011 is available from http://hdl.handle.net/2022/13620. Further versions are forthcoming.This document is designed to be read as a printed document, and designed to permit anyone at all familiar with computers and the Internet to start at the beginning, get a general overview of UITS' advanced IT facilities and what they offer, and then read the detailed portions of the document that are of interest. In many cases, examples are provided, as well as directions on how to download sample files. And in some cases there is information that one is best off really not learning – for example the process of logging into IU's IBM supercomputer the first time involves setup steps that should be followed, keystroke by keystroke, from the directions presented herein, and then promptly forgotten. This document is intended to be a starting point, not a comprehensive guide. As such it should get any reader off to a good start, but then point the reader in the direction of consulting staff and online resources that will permit the reader to get additional help and information as needed. Most of all, this document is provided for the convenience of researchers, who may peruse this information at their leisure. Our hope and expectation is that consultants in UITS will provide extensive help and programming assistance to IU researchers who wish to make use of these excellent IT facilities.The facilities described in this document were made possible in part through funding from Indiana University, the Indiana University Office of the Vice President for Information Technology, the State of Indiana, Shared University Research Grants from IBM, Inc., the National Science Foundation under Grant No. 0116050 and Grant CDA- 9601632, and from the Lilly Endowment through their support of the Indiana Genomics Initiative. The Indiana Genomics Initiative (INGEN) of Indiana University is supported in part by Lilly Endowment Inc