33,113 research outputs found
Recovering Residual Forensic Data from Smartphone Interactions with Cloud Storage Providers
There is a growing demand for cloud storage services such as Dropbox, Box,
Syncplicity and SugarSync. These public cloud storage services can store
gigabytes of corporate and personal data in remote data centres around the
world, which can then be synchronized to multiple devices. This creates an
environment which is potentially conducive to security incidents, data breaches
and other malicious activities. The forensic investigation of public cloud
environments presents a number of new challenges for the digital forensics
community. However, it is anticipated that end-devices such as smartphones,
will retain data from these cloud storage services. This research investigates
how forensic tools that are currently available to practitioners can be used to
provide a practical solution for the problems related to investigating cloud
storage environments. The research contribution is threefold. First, the
findings from this research support the idea that end-devices which have been
used to access cloud storage services can be used to provide a partial view of
the evidence stored in the cloud service. Second, the research provides a
comparison of the number of files which can be recovered from different
versions of cloud storage applications. In doing so, it also supports the idea
that amalgamating the files recovered from more than one device can result in
the recovery of a more complete dataset. Third, the chapter contributes to the
documentation and evidentiary discussion of the artefacts created from specific
cloud storage applications and different versions of these applications on iOS
and Android smartphones
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A-Lister: a tool for analysis of differentially expressed omics entities across multiple pairwise comparisons.
BackgroundResearchers commonly analyze lists of differentially expressed entities (DEEs), such as differentially expressed genes (DEGs), differentially expressed proteins (DEPs), and differentially methylated positions/regions (DMPs/DMRs), across multiple pairwise comparisons. Large biological studies can involve multiple conditions, tissues, and timepoints that result in dozens of pairwise comparisons. Manually filtering and comparing lists of DEEs across multiple pairwise comparisons, typically done by writing custom code, is a cumbersome task that can be streamlined and standardized.ResultsA-Lister is a lightweight command line and graphical user interface tool written in Python. It can be executed in a differential expression mode or generic name list mode. In differential expression mode, A-Lister accepts as input delimited text files that are output by differential expression tools such as DESeq2, edgeR, Cuffdiff, and limma. To allow for the most flexibility in input ID types, to avoid database installation requirements, and to allow for secure offline use, A-Lister does not validate or impose restrictions on entity ID names. Users can specify thresholds to filter the input file(s) by column(s) such as p-value, q-value, and fold change. Additionally, users can filter the pairwise comparisons within the input files by fold change direction (sign). Queries composed of intersection, fuzzy intersection, difference, and union set operations can also be performed on any number of pairwise comparisons. Thus, the user can filter and compare any number of pairwise comparisons within a single A-Lister differential expression command. In generic name list mode, A-Lister accepts delimited text files containing lists of names as input. Queries composed of intersection, fuzzy intersection, difference, and union set operations can then be performed across these lists of names.ConclusionsA-Lister is a flexible tool that enables the user to rapidly narrow down large lists of DEEs to a small number of most significant entities. These entities can then be further analyzed using visualization, pathway analysis, and other bioinformatics tools
Using smartphones as a proxy for forensic evidence contained in cloud storage services
Cloud storage services such as Dropbox, Box and SugarSync have been embraced by both individuals and organizations. This creates an environment that is potentially conducive to security breaches and malicious activities. The investigation of these cloud environments presents new challenges for the digital forensics community.
It is anticipated that smartphone devices will retain data from these storage services. Hence, this research presents a preliminary investigation into the residual artifacts created on an iOS and Android device that has accessed a cloud storage service. The contribution of this paper is twofold. First, it provides an initial assessment on the extent to which cloud storage data is stored on these client-side devices. This view acts as a proxy for data stored in the cloud. Secondly, it provides documentation on the artifacts that could be useful in a digital forensics investigation of cloud services
Source File Set Search for Clone-and-Own Reuse Analysis
Clone-and-own approach is a natural way of source code reuse for software
developers. To assess how known bugs and security vulnerabilities of a cloned
component affect an application, developers and security analysts need to
identify an original version of the component and understand how the cloned
component is different from the original one. Although developers may record
the original version information in a version control system and/or directory
names, such information is often either unavailable or incomplete. In this
research, we propose a code search method that takes as input a set of source
files and extracts all the components including similar files from a software
ecosystem (i.e., a collection of existing versions of software packages). Our
method employs an efficient file similarity computation using b-bit minwise
hashing technique. We use an aggregated file similarity for ranking components.
To evaluate the effectiveness of this tool, we analyzed 75 cloned components in
Firefox and Android source code. The tool took about two hours to report the
original components from 10 million files in Debian GNU/Linux packages. Recall
of the top-five components in the extracted lists is 0.907, while recall of a
baseline using SHA-1 file hash is 0.773, according to the ground truth recorded
in the source code repositories.Comment: 14th International Conference on Mining Software Repositorie
Systems Technology Laboratory (STL) compendium of utilities
Multipurpose programs, routines and operating systems are described. Data conversion and character string comparison subroutine are included. Graphics packages, and file maintenance programs are also included
THE INFLUENCE OF SPECIAL ITEMS TO CORE EARNINGS IN EARNINGS MANAGEMENT AT MANUFACTURING COMPANIES LISTED IN JAKARTA STOCK EXCHANGE
This paper examines the classification of items within the income statement
as an earnings management tool. Evidence is consistent with managers
opportunistically shifting expenses from core expenses (cost of goods sold and
selling, general, and administrative expenses) to special items. This vertical
movement of expenses does not change bottom-line earnings, but overstates ‘‘core’’
earnings.
Keywords: earnings management; earnings components; special items
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VHDL synthesis system (VSS) : user's manual, version 5.0
This report provides instructions for installing and using the VHDL Synthesis System (Version 5.0). VSS is a high level synthesis sytem that synthesizes structures from an abstract description, written with VHDL behavioral constructs. The system uses components from a generic component library (GENUS). The output of VSS is in structural VHDL and could be verified using a commercial VHDL simulator. The designer can control the synthesis process by providing different resource constraints to the system. VSS is also capable of producing different architectures which can be selected by the designer
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