An incremental meta-cognitive-based scaffolding fuzzy neural network

The idea of meta-cognitive learning has enriched the landscape of evolving systems, because it emulates three fundamental aspects of human learning: what-to-learn; how-to-learn; and when-to-learn. However, existing meta-cognitive algorithms still exclude Scaffolding theory, which can realize a plug-and-play classifier. Consequently, these algorithms require laborious pre- and/or post-training processes to be carried out in addition to the main training process. This paper introduces a novel meta-cognitive algorithm termed GENERIC-Classifier (gClass), where the how-to-learn part constitutes a synergy of Scaffolding Theory - a tutoring theory that fosters the ability to sort out complex learning tasks, and Schema Theory - a learning theory of knowledge acquisition by humans. The what-to-learn aspect adopts an online active learning concept by virtue of an extended conflict and ignorance method, making gClass an incremental semi-supervised classifier, whereas the when-to-learn component makes use of the standard sample reserved strategy. A generalized version of the Takagi-Sugeno Kang (TSK) fuzzy system is devised to serve as the cognitive constituent. That is, the rule premise is underpinned by multivariate Gaussian functions, while the rule consequent employs a subset of the non-linear Chebyshev polynomial. Thorough empirical studies, confirmed by their corresponding statistical tests, have numerically validated the efficacy of gClass, which delivers better classification rates than state-of-the-art classifiers while having less complexity.

Towards anomalous diffusion sources detection in a large network

Witnessing the wide spread of malicious information in large networks, we develop an efficient method to detect anomalous diffusion sources and thus protect networks from security and privacy attacks. To date, most existing work on diffusion sources detection are based on the assumption that network snapshots that reflect information diffusion can be obtained continuously. However, obtaining snapshots of an entire network needs to deploy detectors on all network nodes and thus is very expensive. Alternatively, in this article, we study the diffusion sources locating problem by learning from information diffusion data collected from only a small subset of network nodes. Specifically, we present a new regression learning model that can detect anomalous diffusion sources by jointly solving five challenges, that is, unknown number of source nodes, few activated detectors, unknown initial propagation time, uncertain propagation path and uncertain propagation time delay. We theoretically analyze the strength of the model and derive performance bounds. We empirically test and compare the model using both synthetic and real-world networks to demonstrate its performance.

Unknown pattern extraction for statistical network protocol identification

The past decade has seen a lot of research on statistics-based network protocol identification using machine learning techniques. Prior studies have shown promising results in terms of high accuracy and fast classification speed. However, most works have embodied an implicit assumption that all protocols are known in advance and presented in the training data, which is unrealistic since real-world networks constantly witness emerging traffic patterns as well as unknown protocols in the wild. In this paper, we revisit the problem by proposing a learning scheme with unknown pattern extraction for statistical protocol identification. The scheme is designed with a more realistic setting, where the training dataset contains labeled samples from a limited number of protocols, and the goal is to tell these known protocols apart from each other and from potential unknown ones. Preliminary results derived from real-world traffic are presented to show the effectiveness of the scheme.

Cocaine-specific neuroplasticity in the ventral striatum network is linked to delay discounting and drug relapse

AIMS: To contrast functional connectivity on ventral and dorsal striatum networks in cocaine dependence relative to pathological gambling, via a resting-state functional connectivity approach; and to determine the association between cocaine dependence-related neuroadaptations indexed by functional connectivity and impulsivity, compulsivity and drug relapse. DESIGN: Cross-sectional study of 20 individuals with cocaine dependence (CD), 19 individuals with pathological gambling (PG) and 21 healthy controls (HC), and a prospective cohort study of 20 CD followed-up for 12 weeks to measure drug relapse. SETTING AND PARTICIPANTS: CD and PG were recruited through consecutive admissions to a public clinic specialized in substance addiction treatment (Centro Provincial de Drogodependencias) and a public clinic specialized in gambling treatment (AGRAJER), respectively; HC were recruited through community advertisement in the same area in Granada (Spain). MEASUREMENTS: Seed-based functional connectivity in the ventral striatum (ventral caudate and ventral putamen) and dorsal striatum (dorsal caudate and dorsal putamen), the Kirby delay-discounting questionnaire, the reversal-learning task and a dichotomous measure of cocaine relapse indicated with self-report and urine tests. FINDINGS: CD relative to PG exhibit enhanced connectivity between the ventral caudate seed and subgenual anterior cingulate cortex, the ventral putamen seed and dorsomedial pre-frontal cortex and the dorsal putamen seed and insula (P≤0.001, kE=108). Connectivity between the ventral caudate seed and subgenual anterior cingulate cortex is associated with steeper delay discounting (P≤0.001, kE=108) and cocaine relapse (P≤0.005, kE=34). CONCLUSIONS: Cocaine dependence-related neuroadaptations in the ventral striatum of the brain network are associated with increased impulsivity and higher rate of cocaine relapse.

The contribution that alternate, pull-out and externally provided programs within schools make towards student learning, wellbeing and pathways

Research report for Department of Education and Early Childhood Development, Frankston Mornington Peninsula Youth Partnerships & Frankston Mornington Peninsula Local Learning Employment Network. The focus of this review is on programs organised for schools by providers external to the education system for students who are at risk of not completing both compulsory and the non‐compulsory years of schooling and/or who are at risk of low academic achievement. The nature of such risks faced by students and the responses of education institutions to them—and the efficacy of such programs—are also considered.

Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks

Network traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network traffic data. Though the network traffic estimation method has been the most prevalent technique for acquiring network traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network traffic estimation problem is more deteriorated. Besides, the statistical features of network traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network traffic, and then propose a novel network traffic prediction approach based on a deep belief network. We further propose a network traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GÉANT backbone networks.