"Let me Deal so Candidly with the Reader": A Study of the Unnatural Spaces and Narrators of Gulliver’s Travels and the Discworld

Jonathan Swift’s Gulliver’s Travels and Terry Pratchett’s Discworld novels set in Ankh-Morpork are similar enough that both can be treated as belonging to the subgenre of comic fantasy. The narratives foreground the fantastic, written to entertain and amuse its readers but also contain societal criticism in the form of satire or parody. This paper compares the unnatural aspects of Gulliver’s Travels and select City Watch instalments of Discworld. By using a combination of the fairly recent sub-discipline within narratology, unnatural narrative theory, and Genette’s question of “who speaks?”, this study analyses the narrators and the different kinds of unnatural spaces in which they speak. The analysis is divided into four chapters as follows: how to read the unnatural in a narrative, what constitutes an unnatural space, the respective narrator’s voice, and finally, reliability of the narrators within their unnatural space. It becomes apparent that the narrators are unreliable, not only in terms of controlling the information the reader is allowed access to within the narrative but also because of spatiotemporal ambiguity within the narratives

Brothers without Arms: Explaining Iceland’s Participation in European Union CSDP Operations

A number of non-EU members – or third states – have chosen to participate in EU CSDP operations. This thesis seeks to answer why one of them, Iceland, has chosen to do so despite not being a member of the Union and not having any armed forces which are traditionally used in such activities. States have various motivations for participating in international crisis management operations and the same applies to the case of Iceland. The effects of the Cold War coming to an end and a completely transformed international arena have forced Iceland to adjust and seek new means to ensure its own security and interests. As Iceland is almost completely dependent on other actors in security terms, it has sought ways to secure its status within international organizations, including the EU, in order to address this new reality. Various other motivations can also be traced to Iceland participating in CSDP operations, but Iceland’s somewhat hesitant approach points to a state still finding its place in a changing world

Anomaly-Based Intrusion Detection by Modeling Probability Distributions of Flow Characteristics

In recent years, with the increased use of network communication, the risk of compromising the information has grown immensely. Intrusions have evolved and become more sophisticated. Hence, classical detection systems show poor performance in detecting novel attacks. Although much research has been devoted to improving the performance of intrusion detection systems, few methods can achieve consistently efficient results with the constant changes in network communications. This thesis proposes an intrusion detection system based on modeling distributions of network flow statistics in order to achieve a high detection rate for known and stealthy attacks. The proposed model aggregates the traffic at the IP subnetwork level using a hierarchical heavy hitters algorithm. This aggregated traffic is used to build the distribution of network statistics for the most frequent IPv4 addresses encountered as destination. The obtained probability density functions are learned by the Extreme Learning Machine method which is a single-hidden layer feedforward neural network. In this thesis, different sequential and batch learning strategies are proposed in order to analyze the efficiency of this proposed approach. The performance of the model is evaluated on the ISCX-IDS 2012 dataset consisting of injection attacks, HTTP flooding, DDoS and brute force intrusions. The experimental results of the thesis indicate that the presented method achieves an average detection rate of 91% while having a low misclassification rate of 9%, which is on par with the state-of-the-art approaches using this dataset. In addition, the proposed method can be utilized as a network behavior analysis tool specifically for DDoS mitigation, since it can isolate aggregated IPv4 addresses from the rest of the network traffic, thus supporting filtering out DDoS attacks

Serum paraoxonase activity and lipid hydroperoxide levels in adult football players after three days football tournament

Background: It has been suggested that physical activity is an important factor in the prevention and treatment of cardiovascular diseases. Low serum paraoxononase–1 (PON1) activity is with an associated risk of atherosclerotic disease.Objectives: In this study, we aimed to investigate serum PON1 activity and lipid hydroperoxide (LOOH) levels in adult football players after three days football tournament.Methods: Twenty-three adult male football players and 23 sedentary male subjects after three days football tournament were enrolled. Serum paraoxonase, arylesterase activities and LOOH levels were determined.Results: Serum paraoxonase and arylesterase activities were signiûcantly higher in football players than sedentary subjects (all, p<0.05), while LOOH levels were significantly lower (p< 0.05). Serum LOOH levels were inversely correlated with paraoxonase and arylesterase activities (r=-0.552, p<0.001; r=-0.812, p<0.001; respectively) in adult football players.Conclusion: Our data show, for the first time, that physical activity is associated with increased PON1 activity and decreased oxidative stress after three days football tournament. In addition, physical activity for a healthy life is important in increasing serum PON1 activity, and this may play a role in the prevention of atherosclerosis.Key words: Football players, physical activity, PON1 activity, lipid hydroperoxid

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The School as a Democratic Workplace: The Political Dimension of Dewey’s Democracy and Education

In Democracy and Education, John Dewey argued that teachers should have control over their own work. He was, though, not only concerned about workplace democracy for teachers. He also argued against the philosophical underpinnings of educational policies that reproduced social hierarchies in the workplace. The main arguments of Dewey’s book support teachers’ autonomy and students’ equality. When these arguments are read in light of what he wrote about democracy in many other works, they appear to be arguments for workplace democracy. These arguments raise questions about school management that are highly relevant today when prevalent views favor a culture of control

Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods

Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. The framework of statistical learning has gained popularity in the last decade. New methods have been presented to account for the spatial homogeneity of images, to include user's interaction via active learning, to take advantage of the manifold structure with semisupervised learning, to extract and encode invariances, or to adapt classifiers and image representations to unseen yet similar scenes. This tutuorial reviews the main advances for hyperspectral remote sensing image classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201

Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image

Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction algorithms can be employed for recovering the spectral signatures while abundances are estimated using an inversion step. Recent works have shown that exploiting spatial dependencies between image pixels can improve spectral unmixing. Markov random fields (MRF) are classically used to model these spatial correlations and partition the image into multiple classes with homogeneous abundances. This paper proposes to define the MRF sites using similarity regions. These regions are built using a self-complementary area filter that stems from the morphological theory. This kind of filter divides the original image into flat zones where the underlying pixels have the same spectral values. Once the MRF has been clearly established, a hierarchical Bayesian algorithm is proposed to estimate the abundances, the class labels, the noise variance, and the corresponding hyperparameters. A hybrid Gibbs sampler is constructed to generate samples according to the corresponding posterior distribution of the unknown parameters and hyperparameters. Simulations conducted on synthetic and real AVIRIS data demonstrate the good performance of the algorithm