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Learning Malware Analysis
Malware analysis and memory forensics are powerful analysis and investigation techniques used in reverse engineering, digital forensics, and incident response. This book teaches you the concepts, tools, and techniques to determine the behavior and characteristics of malware using malware analysis and memory forensics.
eBook
A Gibbs Energy Minimization Approach for Modeling of Chemical Reactions in a Basic Oxygen Furnace
In modern steelmaking, the decarburization of hot metal is converted into steel primarily in converter processes, such as the basic oxygen furnace. The objective of this work was to develop a new mathematical model for top blown steel converter, which accounts for the complex reaction equilibria in the impact zone, also known as the hot spot, as well as the associated mass and heat transport. An in-house computer code of the model has been developed in Matlab. The main assumption of the model is that all reactions take place in a specified reaction zone. The mass transfer between the reaction volume, bulk slag, and metal determine the reaction rates for the species. The thermodynamic equilibrium is calculated using the partitioning of Gibbs energy (PGE) method. The activity model for the liquid metal is the unified interaction parameter model and for the liquid slag the modified quasichemical model (MQM). The MQM was validated by calculating iso-activity lines for the liquid slag components. The PGE method together with the MQM was validated by calculating liquidus lines for solid components. The results were compared with measurements from literature. The full chemical reaction model was validated by comparing the metal and slag compositions to measurements from industrial scale converter. The predictions were found to be in good agreement with the measured values. Furthermore, the accuracy of the model was found to compare favorably with the models proposed in the literature. The real-time capability of the proposed model was confirmed in test calculations.
Journal Article
Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation
Naïve Bayes (NB) is a well-known probabilistic classification algorithm. It is a simple but efficient algorithm with a wide variety of real-world applications, ranging from product recommendations through medical diagnosis to controlling autonomous vehicles. Due to the failure of real data satisfying the assumptions of NB, there are available variations of NB to cater general data. With the unique applications for each variation of NB, they reach different levels of accuracy. This manuscript surveys the latest applications of NB and discusses its variations in different settings. Furthermore, recommendations are made regarding the applicability of NB while exploring the robustness of the algorithm. Finally, an attempt is given to discuss the pros and cons of NB algorithm and some vulnerabilities, with related computing code for implementation.
Journal Article
Early Warning Signals of Ecological Transitions: Methods for Spatial Patterns
A number of ecosystems can exhibit abrupt shifts between alternative stable states. Because of their important ecological and economic consequences, recent research has focused on devising early warning signals for anticipating such abrupt ecological transitions. In particular, theoretical studies show that changes in spatial characteristics of the system could provide early warnings of approaching transitions. However, the empirical validation of these indicators lag behind their theoretical developments. Here, we summarize a range of currently available spatial early warning signals, suggest potential null models to interpret their trends, and apply them to three simulated spatial data sets of systems undergoing an abrupt transition. In addition to providing a step-by-step methodology for applying these signals to spatial data sets, we propose a statistical toolbox that may be used to help detect approaching transitions in a wide range of spatial data. We hope that our methodology together with the computer codes will stimulate the application and testing of spatial early warning signals on real spatial data.
Journal Article
HiggsBounds-5: testing Higgs sectors in the LHC 13 TeV Era
We describe recent developments of the public computer code HiggsBounds. In particular, these include the incorporation of LHC Higgs search results from Run 2 at a center-of-mass energy of 13 TeV, and an updated and extended framework for the theoretical input that accounts for improved Higgs cross section and branching ratio predictions and new search channels. We furthermore discuss an improved method used in HiggsBounds to approximately reconstruct the exclusion likelihood for LHC searches for non-standard Higgs bosons decaying to
τ
τ
final states. We describe in detail the new and updated functionalities of the new version HiggsBounds-5.
Journal Article
Virtual Infrastructure Management in Private and Hybrid Clouds
One of the many definitions of \"cloud\" is that of an infrastructure-as-a-service (IaaS) system, in which IT infrastructure is deployed in a provider's data center as virtual machines. With IaaS clouds' growing popularity, tools and technologies are emerging that can transform an organization's existing infrastructure into a private or hybrid cloud. OpenNebula is an open source, virtual infrastructure manager that deploys virtualized services on both a local pool of resources and external IaaS clouds. Haizea, a resource lease manager, can act as a scheduling back end for OpenNebula, providing features not found in other cloud software or virtualization-based data center management software.
Journal Article
Ten computer codes that transformed science
From Fortran to arXiv.org, these advances in programming and platforms sent biology, climate science and physics into warp speed.
From Fortran to arXiv.org, these advances in programming and platforms sent biology, climate science and physics into warp speed.
Journal Article
An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics
Background
Bioinformatics researchers are now confronted with analysis of ultra large-scale data sets, a problem that will only increase at an alarming rate in coming years. Recent developments in open source software, that is, the Hadoop project and associated software, provide a foundation for scaling to petabyte scale data warehouses on Linux clusters, providing fault-tolerant parallelized analysis on such data using a programming style named MapReduce.
Description
An overview is given of the current usage within the bioinformatics community of Hadoop, a top-level Apache Software Foundation project, and of associated open source software projects. The concepts behind Hadoop and the associated HBase project are defined, and current bioinformatics software that employ Hadoop is described. The focus is on next-generation sequencing, as the leading application area to date.
Conclusions
Hadoop and the MapReduce programming paradigm already have a substantial base in the bioinformatics community, especially in the field of next-generation sequencing analysis, and such use is increasing. This is due to the cost-effectiveness of Hadoop-based analysis on commodity Linux clusters, and in the cloud via data upload to cloud vendors who have implemented Hadoop/HBase; and due to the effectiveness and ease-of-use of the MapReduce method in parallelization of many data analysis algorithms.
Journal Article