Expressing uncertainty in security analytics research: a demonstration of Bayesian analysis applied to binary classification problems

A common application of security analytics is binary classification problems, which are typically assessed using measures derived from signal detection theory, such as accuracy, sensitivity, and specificity. However, these measures fail to incorporate the uncertainty inherent to many contexts into the results. We propose that the types of binary classification problems studied by security researchers can be described based on the level of uncertainty present in the data. We demonstrate the use of Bayes data analysis in security contexts with varying levels of uncertainty and conclude that Bayesian analysis is particularly relevant in applications characterized by high uncertainty. We discuss how to apply similar analyses to other information security research

Comparing Methods to Extract the Knowledge from Neural Networks

Neural networks (NN) have been shown to be accurate classifiers in many domains. Unfortunately, the lack of NN’s explanatory capability of knowledge learned has somewhat limited their application. A stream of research has therefore developed focusing on knowledge extraction from within neural networks. The literature, unfortunately, lacks consensus on how best to extract knowledge from help neural networks. Additionally, there is a lack of empirical studies that compare existing algorithms on relevant performance measures. Therefore, this study attempts to help fill this gap by comparing two different approaches to extracting IF-THEN rules from feedforward NN. The results show a significant difference in the performance of the two algorithms depending on the structure of the dataset utilized

Pharmacological Analysis of Ionotropic Glutamate Receptor Function in Neuronal Circuits of the Zebrafish Olfactory Bulb

Although synaptic functions of ionotropic glutamate receptors in the olfactory bulb have been studied in vitro, their roles in pattern processing in the intact system remain controversial. We therefore examined the functions of ionotropic glutamate receptors during odor processing in the intact olfactory bulb of zebrafish using pharmacological manipulations. Odor responses of mitral cells and interneurons were recorded by electrophysiology and 2-photon Ca2+ imaging. The combined blockade of AMPA/kainate and NMDA receptors abolished odor-evoked excitation of mitral cells. The blockade of AMPA/kainate receptors alone, in contrast, increased the mean response of mitral cells and decreased the mean response of interneurons. The blockade of NMDA receptors caused little or no change in the mean responses of mitral cells and interneurons. However, antagonists of both receptor types had diverse effects on the magnitude and time course of individual mitral cell and interneuron responses and, thus, changed spatio-temporal activity patterns across neuronal populations. Oscillatory synchronization was abolished or reduced by AMPA/kainate and NMDA receptor antagonists, respectively. These results indicate that (1) interneuron responses depend mainly on AMPA/kainate receptor input during an odor response, (2) interactions among mitral cells and interneurons regulate the total olfactory bulb output activity, (3) AMPA/kainate receptors participate in the synchronization of odor-dependent neuronal ensembles, and (4) ionotropic glutamate receptor-containing synaptic circuits shape odor-specific patterns of olfactory bulb output activity. These mechanisms are likely to be important for the processing of odor-encoding activity patterns in the olfactory bulb

OVERHEATED SECURITY? The Securitisation of Climate Change and the Governmentalisation of Security

Since the mid-2000s, climate change has become one of the defining security issues in political as well as academic debates and amongst others has repeatedly been discussed in the UN Security Council and countless high level government reports in various countries. Beyond the question whether the characterisation as ‘security issue’ is backed up by any robust empirical findings, this begs the question whether the ‘securitisation’ of climate change itself has had tangible political consequences. Moreover, within this research area there is still a lively discussion about which security conceptions apply, how to conceptualise (successful) securitisation and whether it is a (politically and normatively) desirable approach to deal with climate change. The aim of this dissertation is to shed light on these issues and particularly to contribute to a more thorough understanding of different forms or ‘discourses’ of securitisation and their political effects on a theoretical and empirical level. Theoretically, it conceptualises securitisation as resting on different forms of power, which are derived from Michel Foucault’s governmentality lectures. The main argument is that this framework allows me to better capture the ambiguous and diverse variants of securitisation and the ever-changing concept of security as well as to come to a more thorough understanding of the political consequences and powerful effects of constructing issues in terms of security. Empirically, the thesis looks at three country cases, namely the United States, Germany and Mexico. This comparative angle allows me to go beyond the existing literature on the securitisation of climate change that mostly looks at the global level, and to come to a more comprehensive and detailed understanding of different climate security discourses and their political consequences. Concerning the main results, the thesis finds that climate change has indeed been securitised very differently in the three countries and thus has facilitated diverse political consequences. These range from an incorporation of climate change into the defence sector in the US, the legitimisation of far-reaching climate policies in Germany, to the integration of climate change into several civil protection and agricultural insurance schemes in Mexico. Moreover, resting on different forms of power, the securitisation of climate change has played a key role in constructing specific actors and forms of knowledge as legitimate as well as in shaping certain identities in the face of the dangers of climate change. From a normative perspective, neither of these political consequences is purely good or bad but highly ambiguous and necessitates a careful, contextual assessment

Assessing the Effects of Personal Characteristics and Context on U.S. House Speakers’ Leadership Styles, 1789-2006

Research on congressional leadership has been dominated in recent decades by contextual interpretations that see leaders’ behavior as best explained by the environment in which they seek to exercise leadership—particularly, the preference homogeneity and size of their party caucus. The role of agency is thus discounted, and leaders’ personal characteristics and leadership styles are underplayed. Focusing specifically on the speakers of the U.S. House of Representatives from the first to the 110th Congress, we construct measures of each speaker’s commitment to comity and leadership assertiveness. We find the scores reliable and then test the extent to which a speaker’s style is the product of both political context and personal characteristics. Regression estimates on speakers’ personal assertiveness scores provide robust support for a context-plus-personal characteristics explanation, whereas estimates of their comity scores show that speakers’ personal backgrounds trump context

Excitatory amino acidergic pathways and receptors in the basal ganglia

The striatum receives the majority of excitatory amino acidergic input to the basal ganglia from neocortical and allocortical sources. The subthalamic nucleus and the substantia nigra also receive excitatory amino acidergic inputs from neocortex. The subthalamic nucleus, which has prominent projections to the pallidum and nigra, is the only known intrinsic excitatory amino acidergic component of the basal ganglia. Possible excitatory amino acidergic inputs reach the basal ganglia from the intralaminar thalamic nuclei and the pedunculo-pontine nucleus. The striatum is richly endowed with all subtypes of excitatory amino acid receptors and these appear to be inhomogeneously distributed within the striatal complex. The non-striatal nuclei contain lesser levels of excitatory amino acid receptors and the relative proportion of these receptors varies between nuclei. The presence of high densities of excitatory amino acid receptors is a phylogenetically conserved feature of the striatum and its non-mammalian homologues. In Huntington's disease, there is substantial depletion of α -amino-3-hydroxy-5-methylisoxazole-4-propionic acid, N-methyl-D-aspartate, and kainate receptors within the striatum. In Parkinson's disease substantia nigra, there is significant loss of N-methyl-D-aspartate and α -amino-3-hydroxy-5-methylisoxazole-4-propionic acid receptors.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41734/1/726_2004_Article_BF00814003.pd

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Expressing Uncertainty in Information Systems Analytics Research: A Demonstration of Bayesian Analysis Applied to Binary Classification Problems

The measures typically used to assess binary classification problems fail to incorporate the uncertainty inherent to many contexts into the results. We propose using a Bayesian model to express the uncertainty in binary classification problems. This study identified 10 previous studies that provided sufficient data to demonstrate the use of Bayesian analysis in Information Systems (IS) contexts with varying levels of uncertainty. The analysis and user study show that the addition of Bayesian analysis is most useful in high uncertainty contexts with a wide interval for positive predictive value. Such an interval will lead to high uncertainty, even with very certain sensitivity and specificity. The usefulness of Bayesian analysis in conditions of medium uncertainty depends on the context. In conditions of low uncertainty, Bayesian analysis does not add much value. The user study showed that presenting models with uncertainty changed researcher perception of which model performed the best with 18 of 21 researchers changing their opinion. We recommend that authors estimate the uncertainty in their models and provide confusion matrices and prevalence estimates in their results to enable Bayesian analysis as research in a domain matures