81 research outputs found
ENHANCING DIRECTED SEARCH IN BLACK-BOX, GREY-BOX AND WHITE-BOX FUZZ TESTING
Ph.DDOCTOR OF PHILOSOPH
Predicting High-Cap Tech Stock Polarity: A Combined Approach using Support Vector Machines and Bidirectional Encoders from Transformers
The abundance, accessibility, and scale of data have engendered an era where machine learning can quickly and accurately solve complex problems, identify complicated patterns, and uncover intricate trends. One research area where many have applied these techniques is the stock market. Yet, financial domains are influenced by many factors and are notoriously difficult to predict due to their volatile and multivariate behavior. However, the literature indicates that public sentiment data may exhibit significant predictive qualities and improve a model’s ability to predict intricate trends. In this study, momentum SVM classification accuracy was compared between datasets that did and did not contain sentiment analysis-related features. The results indicated that sentiment containing datasets were typically better predictors, with improved model accuracy. However, the results did not reflect the improvements shown by similar research and will require further research to determine the nature of the relationship between sentiment and higher model performance
Principles of Security and Trust
This open access book constitutes the proceedings of the 8th International Conference on Principles of Security and Trust, POST 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conference on Theory and Practice of Software, ETAPS 2019. The 10 papers presented in this volume were carefully reviewed and selected from 27 submissions. They deal with theoretical and foundational aspects of security and trust, including on new theoretical results, practical applications of existing foundational ideas, and innovative approaches stimulated by pressing practical problems
Principles of Security and Trust
This open access book constitutes the proceedings of the 8th International Conference on Principles of Security and Trust, POST 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conference on Theory and Practice of Software, ETAPS 2019. The 10 papers presented in this volume were carefully reviewed and selected from 27 submissions. They deal with theoretical and foundational aspects of security and trust, including on new theoretical results, practical applications of existing foundational ideas, and innovative approaches stimulated by pressing practical problems
Data Warehousing Modernization: Big Data Technology Implementation
Considering the challenges posed by Big Data, the cost to scale traditional data warehouses is high and the performances would be inadequate to meet the growing needs of the volume, variety and velocity of data. The Hadoop ecosystem answers both of the shortcomings. Hadoop has the ability to store and analyze large data sets in parallel on a distributed environment but cannot replace the existing data warehouses and RDBMS systems due to its own limitations explained in this paper. In this paper, I identify the reasons why many enterprises fail and struggle to adapt to Big Data technologies. A brief outline of two different technologies to handle Big Data will be presented in this paper: Using IBM’s Pure Data system for analytics (Netezza) usually used in reporting, and Hadoop with Hive which is used in analytics. Also, this paper covers the Enterprise architecture consisting of Hadoop that successful companies are adapting to analyze, filter, process, and store the data running along a massively parallel processing data warehouse. Despite, having the technology to support and process Big Data, industries are still struggling to meet their goals due to the lack of skilled personnel to study and analyze the data, in short data scientists and data statisticians
Faults in Linux 2.6
In August 2011, Linux entered its third decade. Ten years before, Chou et al.
published a study of faults found by applying a static analyzer to Linux
versions 1.0 through 2.4.1. A major result of their work was that the drivers
directory contained up to 7 times more of certain kinds of faults than other
directories. This result inspired numerous efforts on improving the reliability
of driver code. Today, Linux is used in a wider range of environments, provides
a wider range of services, and has adopted a new development and release model.
What has been the impact of these changes on code quality? To answer this
question, we have transported Chou et al.'s experiments to all versions of
Linux 2.6; released between 2003 and 2011. We find that Linux has more than
doubled in size during this period, but the number of faults per line of code
has been decreasing. Moreover, the fault rate of drivers is now below that of
other directories, such as arch. These results can guide further development
and research efforts for the decade to come. To allow updating these results as
Linux evolves, we define our experimental protocol and make our checkers
available
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
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