347,257 research outputs found
Master of Science
thesisPolitical scientists believe that the Abu Sayyaf Group's (ASG) penchant for accumulating funds through hostage ransoming and other criminal activity has caused the organization to drift away from its Islamic foundations to become bandits. This research explores this claim by applying geospatial analysis to ASG terrorist activity to evaluate if ASG attack data are congruent with original political objectives or more suited to profitdriven criminal patterns of activity. Four research objectives are used that explore if attack data display an operational shift. The first three compare data distribution to map overlays of economic level, ethnicity and religion to identify where attack majorities occur. This identifies if the ASG is prone to attacking areas populated by their constituency. The fourth objective examines the history of the ASG by comparing it to terrorist ideological transformation theory. The results of these objectives are combined in the decision rule to evaluate if ASG data supports the claims of a philosophical shift. Applied methods include spatio-temporal analysis and geostatistics (hot spot analysis and mean center progression). Results of analysis indicate that the majority of ASG attacks occur in a trivariate convergence area of map overlays. Temporal analysis shows that attacks localized and peaked around the Constituency Overlay in accordance with benchmarks for a terror-to-crime shift. It is concluded that the majority of ASG attacks are driven towards crime due to a high frequency of moneymaking attacks within areas of constituency. Based on the decision rule, the patterns of attack data indicate that ASG operations have been more inclined towards criminal goals since the death of their founder, Abdurajak Janjalani
Encoding Markov Logic Networks in Possibilistic Logic
Markov logic uses weighted formulas to compactly encode a probability
distribution over possible worlds. Despite the use of logical formulas, Markov
logic networks (MLNs) can be difficult to interpret, due to the often
counter-intuitive meaning of their weights. To address this issue, we propose a
method to construct a possibilistic logic theory that exactly captures what can
be derived from a given MLN using maximum a posteriori (MAP) inference.
Unfortunately, the size of this theory is exponential in general. We therefore
also propose two methods which can derive compact theories that still capture
MAP inference, but only for specific types of evidence. These theories can be
used, among others, to make explicit the hidden assumptions underlying an MLN
or to explain the predictions it makes.Comment: Extended version of a paper appearing in UAI 201
Pinwheel Scheduling for Fault-tolerant Broadcast Disks in Real-time Database Systems
The design of programs for broadcast disks which incorporate real-time and fault-tolerance requirements is considered. A generalized model for real-time fault-tolerant broadcast disks is defined. It is shown that designing programs for broadcast disks specified in this model is closely related to the scheduling of pinwheel task systems. Some new results in pinwheel scheduling theory are derived, which facilitate the efficient generation of real-time fault-tolerant broadcast disk programs.National Science Foundation (CCR-9308344, CCR-9596282
Engineering Object-Oriented Semantics Using Graph Transformations
In this paper we describe the application of the theory of graph transformations to the practise of language design. We have defined the semantics of a small but realistic object-oriented language (called TAAL) by mapping the language constructs to graphs and their operational semantics to graph transformation rules. In the process we establish a mapping between UML models and graphs.
TAAL was developed for the purpose of this paper, as an extensive case study in engineering object-oriented language semantics using graph transformation. It incorporates the basic aspects of many commonly used object-oriented programming languages: apart from essential imperative programming constructs, it includes inheritance, object creation and method overriding. The language specification is based on a number of meta-models written in UML.
Both the static and dynamic semantics are defined using graph rewriting rules.
In the course of the case study, we have built an Eclipse plug-in that automatically transforms arbitrary TAAL programs into graphs, in a graph format readable by another tool. This second tool is called Groove, and it is able to execute graph transformations. By combining both tools we are able to visually simulate the execution of any TAAL program
Threats Management Throughout the Software Service Life-Cycle
Software services are inevitably exposed to a fluctuating threat picture.
Unfortunately, not all threats can be handled only with preventive measures
during design and development, but also require adaptive mitigations at
runtime. In this paper we describe an approach where we model composite
services and threats together, which allows us to create preventive measures at
design-time. At runtime, our specification also allows the service runtime
environment (SRE) to receive alerts about active threats that we have not
handled, and react to these automatically through adaptation of the composite
service. A goal-oriented security requirements modelling tool is used to model
business-level threats and analyse how they may impact goals. A process flow
modelling tool, utilising Business Process Model and Notation (BPMN) and
standard error boundary events, allows us to define how threats should be
responded to during service execution on a technical level. Throughout the
software life-cycle, we maintain threats in a centralised threat repository.
Re-use of these threats extends further into monitoring alerts being
distributed through a cloud-based messaging service. To demonstrate our
approach in practice, we have developed a proof-of-concept service for the Air
Traffic Management (ATM) domain. In addition to the design-time activities, we
show how this composite service duly adapts itself when a service component is
exposed to a threat at runtime.Comment: In Proceedings GraMSec 2014, arXiv:1404.163
An automated ETL for online datasets
While using online datasets for machine learning is commonplace today, the quality of these datasets impacts on the performance
of prediction algorithms. One method for improving the semantics of new data sources is to map these sources to a common
data model or ontology. While semantic and structural heterogeneities must still be resolved, this provides a well established
approach to providing clean datasets, suitable for machine learning and analysis. However, when there is a requirement for a
close to real time usage of online data, a method for dynamic Extract-Transform-Load of new sources data must be developed.
In this work, we present a framework for integrating online and enterprise data sources, in close to real time, to provide
datasets for machine learning and predictive algorithms. An exhaustive evaluation compares a human built data transformation
process with our system’s machine generated ETL process, with very favourable results, illustrating the value and impact of
an automated approach
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