Technology forecasting in the National Research and Education Network technology domain using context sensitive Data Fusion

Using inductive reasoning this paper develops a framework for the Structural Equation Modeling based context sensitive Data Fusion of technology indicators in order to produce Technology Forecasting output metrics. Data Fusion is a formal framework that defines tools, as well as the application of these tools, for the unification of data originating from diverse sources. Context sensitive Data Fusion techniques refine the generated knowledge using the characteristics of exogenous context related variables, which in the proposed framework entails non-technology related metrics. Structural Equation Modeling, which is a statistical technique capable of evaluating complex hierarchical dependencies between latent and observed constructs, has been shown to be effective in implementing context sensitive Data Fusion. For illustrative purposes an example model instantiation of the proposed framework is constructed for the case of the National Research and Education Network technology domain using knowledge gained through action research in the South African National Research Network, hypotheses from peer-reviewed literature and insights from the Trans- European Research and Education Network Association’s annual compendiums for National Research and Education Network infrastructure and services trends. This example model instantiation hypothesizes that a National Research and Education Network’s infrastructure and advanced services capabilities are positively related to one another, as well as to the contextual influence it experiences through government control. Also, positive relationships are hypothesized between a National Research and Education Network’s infrastructure and advanced services capabilities and its usage, which is defined as the technology forecasting output metric of interest for this example. Data from the 2011 Trans-European Research and Education Network Association compendium is used in the Partial Least Square regression analysis of the example model instantiation, which confirms all hypothesized relationships, except the postulation that a National Research and Education Network’s infrastructure and advanced services capabilities are positively related. This latter finding is explained by observing the prevalence of technology leapfrogging in the National Research and Education Network global community.The Council for Scientific and Industrial Research, as well as the University of Pretoria.http://www.journals.elsevier.com/technological-forecasting-and-social-change2017-10-31hb2016Graduate School of Technology Management (GSTM

Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill posed problem that commonly arises when modelling real world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of (ie, hypotheses about) network architectures and implicit coupling functions in terms of their Bayesian model evidence. These methods are collectively referred to as dynamical casual modelling (DCM). We focus on a relatively new approach that is proving remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems

WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking

We present WISER, a new semantic search engine for expert finding in academia. Our system is unsupervised and it jointly combines classical language modeling techniques, based on text evidences, with the Wikipedia Knowledge Graph, via entity linking. WISER indexes each academic author through a novel profiling technique which models her expertise with a small, labeled and weighted graph drawn from Wikipedia. Nodes in this graph are the Wikipedia entities mentioned in the author's publications, whereas the weighted edges express the semantic relatedness among these entities computed via textual and graph-based relatedness functions. Every node is also labeled with a relevance score which models the pertinence of the corresponding entity to author's expertise, and is computed by means of a proper random-walk calculation over that graph; and with a latent vector representation which is learned via entity and other kinds of structural embeddings derived from Wikipedia. At query time, experts are retrieved by combining classic document-centric approaches, which exploit the occurrences of query terms in the author's documents, with a novel set of profile-centric scoring strategies, which compute the semantic relatedness between the author's expertise and the query topic via the above graph-based profiles. The effectiveness of our system is established over a large-scale experimental test on a standard dataset for this task. We show that WISER achieves better performance than all the other competitors, thus proving the effectiveness of modelling author's profile via our "semantic" graph of entities. Finally, we comment on the use of WISER for indexing and profiling the whole research community within the University of Pisa, and its application to technology transfer in our University

Factorial Structure and Measurement Invariance of the Acceptance and Action Questionnaire-Stigma (AAQ-S) in Spain

The objective of the present study was to validate and adapt the Acceptance and Action Questionnaire-Stigma (AAQ-S) to the Spanish context. Method: The study included the participation of 1212 subjects, with an average age of 17.12 years old. Results: The confirmatory factorial analysis revealed a number of adequate fit indices for the new version of the scale χ2/df = 3.24; Comparative Fit Index = 0.96; Incremental Fit Index = 0.96; Root Mean Square Error of Approximation = 0.060; Standardized Root Mean Square Residual = 0.035, in which the factorial structures displayed gender invariance. The two factors comprise the scale both exhibited high internal consistency (+0.90) and temporal stability. Conclusion: The Spanish version of the AAQ-S proved to be a robust and adequate psychometric instrument. In this sense, future lines of research focused on determining the role of psychological flexibility in stigma and the processes of change at the base of interventions could benefit substantially from the use of AAQ-S