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.

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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

Fairness is not sufficiently formalized to enable the comparison of different fairness requirements. Ad-hoc collaborations in the Internet of Services are driven by trust, and fairness in such collaborations is an expectation that builds trust. We provide formal fairness definitions along with a formal fairness model, which augment collaboration establishment and support building trust. Hence, we are able to reason about fairness and its implications for negotiations.

Formalizing and Modeling Fairness for Benefit Distribution in Collaborations

Interactions between individuals are inherently dependent upon trust, no matter if they occur in the real world or in cybercommunities Over the past years, proposals have been made to model trust relations computationally, either to assist users or for modeling purposes in multi-agent systems These models rely implicitly on the social networks established by participating entities (be they autonomous agents or internet users) However, state-of-the-art trust frameworks often neglect the structure of those complex networks In this paper, we present a new approach allowing agent-based trust frameworks to leverage information from both trusted and untrusted witnesses that would otherwise be neglected An effective and robust voting scheme based on an agreement metric is presented and its benefit is shown through simulations

Group-agreement as a reliability measure for witness recommendations in reputation-based trust protocols

To ensure the integration and a systematic performance evaluation of the CubICA, the JADE and the EfICA algorithm, a previously developed testsuite for BSS-problems is used. All the steps to perform a competition of methods for source separation are part of a testsuite that supports the development and evaluation of blind source separation (BSS) algorithms in a highly automated way. The concept of the testsuite is presented and it is shown how the testsuite can be used to apply a selection of BSS-algorithms to four standard subproblems. 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. Also the integration of CubICA, JADE and the EfICA algorithm and the results achieved from automated tests and parameter optimizations will be described

An algorithm evaluation test suite for blind source separation problem

Maintaining the continuous supply of electricity within smart microgrids is a challenging task, which becomes increasingly difficult with the growing integration of volatile Renewable Energy Sources (RES). Quick changes within the production behavior of these resources can disturb the necessary balance between demand and supply and may ultimately lead to blackouts within the grid. To prevent balance disturbances, electricity production and consumption needs to be coordinated
and power needs to be shared among the participants within
the microgrid. Facilitating coordinated behavior of grid entities
and ensuring reliable operation of the microgrid in the presence of volatile RES requires sophisticated strategies for operating individual participants. In this paper, we present a modular framework to support dynamic energy distribution for atomic entities (producers/consumers) in holarchically organized energy grids. In particular, the framework provides production and consumption forecasting to enable intelligent strategy selection to improve the day-ahead control decisions for atomic entities. The proposed framework enables the bottom-up formation of smart mircogrid holons and represents a foundation for the formation and strategic coordination of participants in smart microgrid.

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A Framework for Strategy Selection of Atomic Entities in the Holonic Smart Grid

Online Social Networks (OSNs) are the core of most communications nowadays, leading to possibly sensitive information exchange. Privacy is an important building block of free societies, and thus, for OSNs. OSNs function as group communication systems and can be build in centralised and distributed styles. Privacy can be achieved in distributed systems as all participants contribute to privacy. Peer-to-peer-based group communication systems achieve this privacy improvement partially, at the cost of additional messaging overhead. In this paper, we introduce ant colony optimisation to reduce the messaging overhead of anonymous communication systems, bridging the gap between privacy and efficiency. We apply our adapted privacy sensitive ant colony optimization to improve routing paths by encouraging re-usage and aggregation. Our first results indicate a 9–13% lower messaging overhead compared to the state of the art, while maintaining privacy.

Sascha Hauke

Papers

Formalizing and Modeling Fairness for Benefit Distribution in Collaborations

Group-agreement as a reliability measure for witness recommendations in reputation-based trust protocols

An algorithm evaluation test suite for blind source separation problem

A Framework for Strategy Selection of Atomic Entities in the Holonic Smart Grid

Ant colony optimisation — A solution to efficient anonymous group communication?