Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

Neuropsychiatric disorders are associated with abnormal function of the default mode network (DMN), a distributed network of brain regions more active during rest than during performance of many attention-demanding tasks and characterized by a high degree of functional connectivity (i.e., temporal correlations between brain regions). Functional magnetic resonance imaging studies have revealed that the DMN in the healthy brain is associated with stimulus-independent thought and self-reflection and that greater suppression of the DMN is associated with better performance on attention-demanding tasks. In schizophrenia and depression, the DMN is often found to be hyperactivated and hyperconnected. In schizophrenia this may relate to overly intensive self-reference and impairments in attention and working memory. In depression, DMN hyperactivity may be related to negative rumination. These findings are considered in terms of what is known about psychological functions supported by the DMN, and alteration of the DMN in other neuropsychiatric disorders.

Default Mode Network Activity and Connectivity in Psychopathology

Network intrusion detection systems (NIDSs) play a crucial role in defending computer networks. However, there are concerns regarding the feasibility and sustainability of current approaches when faced with the demands of modern networks. More specifically, these concerns relate to the increasing levels of required human interaction and the decreasing levels of detection accuracy. This paper presents a novel deep learning technique for intrusion detection, which addresses these concerns. We detail our proposed nonsymmetric deep autoencoder (NDAE) for unsupervised feature learning. Furthermore, we also propose our novel deep learning classification model constructed using stacked NDAEs. Our proposed classifier has been implemented in graphics processing unit (GPU)-enabled TensorFlow and evaluated using the benchmark KDD Cup ’99 and NSL-KDD datasets. Promising results have been obtained from our model thus far, demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs.

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A Deep Learning Approach to Network Intrusion Detection

The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved. We here present a review of the methods most commonly applied in this rapidly advancing field, such as seed-based correlation analysis and independent component analysis, along with examples of their use at the individual subject and group analysis levels and a discussion of practical and theoretical issues arising from this data ‘explosion’. We describe the similarities and differences across these varied statistical approaches to processing resting-state FMRI signals, and conclude that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture.

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Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data

The posterior cingulate cortex (PCC) is a central part of the default mode network (DMN) and part of the structural core of the brain. Although the PCC often shows consistent deactivation when attention is focused on external events, anatomical studies show that the region is not homogeneous, and electrophysiological recordings in nonhuman primates suggest that it is directly involved in some forms of attention. We report a functional magnetic resonance imaging study of an attentionally demanding task (either a zero- or two-back working memory task). Standard subtraction analysis within the PCC shows a relative deactivation as task difficulty increases. In contrast, a dual-regression functional connectivity analysis reveals a clear dissociation between ventral and dorsal parts of the PCC. As task difficulty increases, the ventral PCC shows reduced integration within the DMN and less anticorrelation with the cognitive control network (CCN) activated by the task. The dorsal PCC shows an opposite pattern, with increased DMN integration and more anticorrelation. At rest, the dorsal PCC also shows functional connectivity with both the DMN and attentional networks. As expected, these results provide evidence that the PCC is involved in supporting internally directed thought, as the region is more highly integrated with the DMN at low task demands. In contrast, the task-dependent increases in connectivity between the dorsal PCC and the CCN are consistent with a role for this region in modulating the dynamic interaction between these two networks controlling the efficient allocation of attention.

https://www.jneurosci.org/content/jneuro/31/9/3217.full.pdf

Fractionating the Default Mode Network: Distinct Contributions of the Ventral and Dorsal Posterior Cingulate Cortex to Cognitive Control

The fusion of trust relevant information provided by multiple sources is one of the major challenges of trust establishment, which in turn is a key research topic in the growing field of cloud computing We present a novel fusion operator for combining information from different sources, representing propositions under uncertainty The operator especially extend the state-of-the-art by explicitly considering weights and the handling of conflicting dependent opinions We provide a use case that demonstrates the applicability of our approach and shows the capability of the novel operator to a more reliable and transparent assessment of the trustworthiness of cloud providers

Fusion of Opinions under Uncertainty and Conflict -- Application to Trust Assessment for Cloud Marketplaces

The visualization and exploration of neuroimaging data is important for the analysis of anatomical and functional magnetic resonance (MR) images and thresholded statistical parametric maps. While two-dimensional orthogonal views of neuroimaging data are used to display statistical analyses, real three-dimensional (3d) depictions are helpful for showing the spatial distribution of a functional network, as well as its temporal evolution. However, viewers that are freely available on the internet offer only limited rendering capabilities and depictions of temporal changes of the blood oxygen level-dependent (BOLD) response. In this article, we present BrainBlend, a toolbox for the software package Statistical Parametric Mapping (SPM), that generates voxeldata files to be used with the open-source 3d-software "Blender". Our interface between SPM and Blender permits the use of any Analyze- and Nifti-file for the creation of images and animations of transparent volumetric objects. Different kinds of anatomical, functional and statistical data can be rendered as volumetric objects in order to convey an immediate understanding of the three-dimensional shape. Representations of functional networks can be animated using a time course extracted from the general linear model or the independent component analysis. Relative BOLD activations of functional MR-images can be calculated for a time-resolved depiction of hemodynamic changes. The resulting animation can be displayed along with its corresponding paradigm matrix and the presented stimuli. BrainBlend is particularly suitable for the visual exploration of interactions between functional networks, for time-resolved animations of BOLD changes and meets high demands on visual quality in images and animations.

fMRI Data Visualization with BrainBlend and Blender

Dynamic causal modeling with genetic algorithms

In this study we used a Random Forest-based approach for an assignment of small guanosine triphosphate proteins (GTPases) to specific subgroups. Small GTPases represent an important functional group of proteins that serve as molecular switches in a wide range of fundamental cellular processes, including intracellular transport, movement and signaling events. These proteins have further gained a special emphasis in cancer research, because within the last decades a huge variety of small GTPases from different subgroups could be related to the development of all types of tumors. Using a random forest approach, we were able to identify the most important amino acid positions for the classification process within the small GTPases superfamily and its subgroups. These positions are in line with the results of earlier studies and have been shown to be the essential elements for the different functionalities of the GTPase families. Furthermore, we provide an accurate and reliable software tool (GTPasePred) to identify potential novel GTPases and demonstrate its application to genome sequences.

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Insights into the classification of small GTPases.

State-of-the art trust and reputation systems seek to apply machine learning methods to overcome generalizability issues of experience-based Bayesian trust assessment. These approaches are, however, often model-centric instead of focussing on data and the complex adaptive system that is driven by reputation-based service selection. This entails the risk of unrealistic model assumptions. We outline the requirements for robust probabilistic trust assessment using supervised learning and apply a selection of estimators to a real-world dataset, in order to show the effectiveness of supervised methods. Furthermore, we provide a representational mapping of estimator output to a belief logic representation for the modular integration of supervised methods with other trust assessment methodologies.

Sascha Hauke

Papers

Fusion of Opinions under Uncertainty and Conflict -- Application to Trust Assessment for Cloud Marketplaces

fMRI Data Visualization with BrainBlend and Blender

Dynamic causal modeling with genetic algorithms

Insights into the classification of small GTPases.

On the Application of Supervised Machine Learning to Trustworthiness Assessment