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

This work describes the incorporation of human strategies into a genetic algorithm Human competence and machine intelligence are merged, creating a divide and evolve approach The approach is applied to the restoration problem of Rubik's Cube and successfully solves this task

Divide and evolve driven by human strategies

Digital archiving systems are necessary to store documents for several years, such as electronic health records. However, security breaches in these systems may allow attackers to tamper with archived documents without being noticed. To address this threat, standardized archiving systems require a public key infrastructure, where a time-stamp authority is trusted to date and sign stored documents periodically. However, in practice a time-stamp authority may not be fully trustworthy, allowing an
attacker to forge documents. Thus, in this paper, we introduce a novel reputation-based trust system for time-stamping-based archiving called Long-term evaluation of Trust (LoT), which alleviates the required trust assumptions. This makes LoT an important contribution to realize trust and security management for digital archiving systems using public key infrastructures. We implemented LoT showcasing its applicability to electronic health records and demonstrate its efficacy by simulations.

/pdf/lot-a-reputation-based-trust-system-for-long-term-archiving-468vs60gqc.pdf

LoT: a Reputation-based Trust System for Long-term Archiving

Trust and reputation systems (TRS) require a composition of individual methods for prediction, selection and feedback/data generation in order to fully realise their potential Building on recent work identifying common aspects of TRS, this paper presents a prototype of a software deployment tool for assembling TRS from individual modules This tool is considered as a precursory support step to the systematic testing and comparison of the various facets that constitute the trust assessment process This will support future research and provide an engineering perspective to the development of new TRS, by providing easy deployment of TRS for testing

Towards engineering trust systems: Template-based, component-oriented assembly

Feature extraction methods are widely applied in order to reduce the dimensionality of data for subsequent classification, thus decreasing the risk of noise fitting. Principal Component Analysis (PCA) is a popular linear method for transforming high-dimensional data into a low-dimensional representation. Non-linear and non-parametric methods for dimension reduction, such as Isomap, Stochastic Neighbor Embedding (SNE) and Interpol are also used. In this study, we compare the performance of PCA, Isomap, t-SNE and Interpol as preprocessing steps for classification of protein sequences. Using random forests, we compared the classification performance on two artificial and eighteen real-world protein data sets, including HIV drug resistance, HIV-1 co-receptor usage and protein functional class prediction, preprocessed with PCA, Isomap, t-SNE and Interpol. Significant differences between these feature extraction methods were observed. The prediction performance of Interpol converges towards a stable and significantly higher value compared to PCA, Isomap and t-SNE. This is probably due to the nature of protein sequences, where amino acid are often dependent from and affect each other to achieve, for instance, conformational stability. However, visualization of data reduced with Interpol is rather unintuitive, compared to the other methods. We conclude that Interpol is superior to PCA, Isomap and t-SNE for feature extraction previous to classification, but is of limited use for visualization.

Unsupervised Dimension Reduction Methods for Protein Sequence Classification

A first step to perform a competition of methods for source separation is the development of a testsuite that supports the development and evaluation of blind source separation (BSS) algorithms in a highly automated way. The concept of our testsuite is presented and it is shown how the testsuite can be used to apply BSS-algorithms to four standard sub-problems. To compare the performance of arbitrary algorithms on given problems the testsuite allows the integration of new algorithms and testing problems using well defined interfaces. A brief example is given by the integration of the FlexICA, EVD, EVD24 and the FastICA algorithm and our results achieved from automated tests and parameter optimizations.

http://www.wseas.us/e-library/conferences/2006istanbul/papers/521-175.pdf

Sascha Hauke

Papers

Divide and evolve driven by human strategies

LoT: a Reputation-based Trust System for Long-term Archiving

Towards engineering trust systems: Template-based, component-oriented assembly

Unsupervised Dimension Reduction Methods for Protein Sequence Classification

Automated BSS-algorithm performance evaluation