15,341 research outputs found
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
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The effects of school reform under NCLB waivers: Evidence from focus schools in Kentucky.
Under waivers to the No Child Left Behind (NCLB) Act, the federal government required states to identify schools where targeted subgroups of students have the lowest achievement and to implement reforms in these “Focus Schools.” In this study, we examine the Focus School reforms in the state of Kentucky. The reforms in this state are uniquely interesting for several reasons. One is that the state developed unusually explicit guidance for Focus Schools centered on a comprehensive school-planning process. Second, the state identified Focus Schools using a “super subgroup” measure that combined traditionally low-performing subgroups into an umbrella group. This design feature may have catalyzed broader whole-school reforms and attenuated the incentives to target reform efforts narrowly. Using regression discontinuity designs, we find that these reforms led to substantial improvements in school performance, raising math achievement by 17 percent and reading achievement by 9 percent
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
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Does Revolution Work? Evidence from Nepal’s People’s War
In 2015, after a decade-long conflict and nine years of negotiation, Nepal promulgated a constitution that replaced its 240-year-old monarchy by a federal republic. The subsequent 2017 local elections ushered more than 30,000 first-time politicians into office. Using a census of 3.68 million Nepalis (2.56 million of whom are of voting age) covering eleven districts, party nomination lists and party candidate selection committee surveys, electoral data and information on conflict incidence, we document that castes that were historically excluded from political representation achieved representation without a significant representation-ability trade-off: improved social representation among politicians is accompanied by positive selection on education and income. Triangulating across multiple data sources, we show that the entry of the revolutionary Maoist group as a post-conflict mainstream party played an important role. Finally, political representation of non-elite castes improved their policy inclusion as measured by individual access to earthquake reconstruction transfers. These gains, however, vary with the extent of social connections to the elected mayor and point to a continuing need to balance power by supporting institutions that provide all citizens political voice
Early aspects: aspect-oriented requirements engineering and architecture design
This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications
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