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.

/pdf/a-deep-learning-approach-to-network-intrusion-detection-n63kpq7big.pdf

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.

/pdf/advances-and-pitfalls-in-the-analysis-and-interpretation-of-40olc4x1hh.pdf

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

Visualisations are often used to communicate trust-worthiness to end users. Showing a number of stars, for example, is a well-known practice in e-commerce applications to communicate the quality of a product or service. Many products or services also have their quality - in terms of trustworthiness - described along more than one dimension, so that not only an overall trust score has to be communicated, but multiple scores, one for each dimension. Current visualisations of such a multicriterial trustworthiness are often based on the display of multiple individual star-like interfaces - a practice that offers room for improvement with regard to intuitive understanding of the displayed trust information. In this paper, we present T-Viz, a trust visualisation based on radar plots and pie charts. T-Viz concurrently shows multiple trust scores, one for each dimension, along with an aggregated trust score. Moreover, T-Viz also shows a reliability measure for every trust score graphically, in the form of a certainty score. The evaluation results from a pilot study with eleven participants indicate that T-Viz is an intuitive, comprehensible and clear interface. It succeeds at visualising and communicating multicriterial trust scores under uncertainty in one, easy to understand, graphical representation.

Communicating and visualising multicriterial trustworthiness under uncertainty

Breeding Cell Penetrating Peptides: Optimization of Cellular Uptake by a Function-Driven Evolutionary Process.

In this chapter, we provide an in-depth insight into computational trust methods that are able to reliably quantify the security level of service providers and transparently communicate that level to the users. The methods particularly consider business as well as end user requirements along with a complex specification of security assurances during security quantification. Novel trust methods are validated using formal proofs, industry-accepted security assurance datasets, and user studies.

Computational trust methods for security quantification in the cloud ecosystem

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 capabilities of our operators to a more reliable and transparent assessment of the trustworthiness of cloud providers.

Fusion of Opinions under Uncertainty and Conflict -- Trust Assessment for Cloud Marketplaces (Full version)

Reputation-based trust models are essentially reinforcement learning mechanisms reliant on feedback As such, they face a cold start problem when attempting to assess an unknown service partner Stateof-the-art models address this by incorporating dispositional knowledge, the derivation of which is not described regularly We propose three mechanisms for integrating knowledge readily available in cyber-physical services (eg, online ordering) to determine the trust disposition of consumers towards unknown services (and their providers) These reputationbuilding indicators of trustworthiness can serve as cues for trust-based decision making in eCommerce scenarios and drive the evolution of reputation-based trust models towards trust management systems

Sascha Hauke

Papers

Communicating and visualising multicriterial trustworthiness under uncertainty

Breeding Cell Penetrating Peptides: Optimization of Cellular Uptake by a Function-Driven Evolutionary Process.

Computational trust methods for security quantification in the cloud ecosystem

Fusion of Opinions under Uncertainty and Conflict -- Trust Assessment for Cloud Marketplaces (Full version)

Integrating Indicators of Trustworthiness into Reputation-Based Trust Models - Insurance, Certification, and Coalitions.