Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

Compromised user credentials detection in a digital enterprise using behavioral analytics

© 2018 In today\u27s digital age, the digital transformation is necessary for almost every competitive enterprise in terms of having access to the best resources and ensuring customer satisfaction. However, due to such rewards, these enterprises are facing key concerns around the risk of next-generation data security or cybercrime which is continually increasing issue due to the digital transformation four essential pillars—cloud computing, big data analytics, social and mobile computing. Data transformation-driven enterprises should ready to handle this next-generation data security problem, in particular, the compromised user credential (CUC). When an intruder or cybercriminal develops trust relationships as a legitimate account holder and then gain privileged access to the system for misuse. Many state-of-the-art risk mitigation tools are being developed, such as encrypted and secure password policy, authentication, and authorization mechanism. However, the CUC has become more complex and increasingly critical to the digital transformation process of the enterprise\u27s database by a cybercriminal, we propose a novel technique that effectively detects CUC at the enterprise-level. The proposed technique is learning from the user\u27s behavior and builds a knowledge base system (KBS) which observe changes in the user\u27s operational behavior. For that reason, a series of experiments were carried out on the dataset that collected from a sensitive database. All empirical results are validated through well-known evaluation measures, such as (i) accuracy, (ii) sensitivity, (iii) specificity, (iv) prudence accuracy, (v) precision, (vi) f-measure, and (vii) error rate. The experiments show that the proposed approach obtained weighted accuracy up to 99% and overall error of about 1%. The results clearly demonstrate that the proposed model efficiently can detect CUC which may keep an organization safe from major damage in data through cyber-attacks

A review of applied methods in Europe for flood-frequency analysis in a changing environment

The report presents a review of methods used in Europe for trend analysis, climate change projections and non-stationary analysis of extreme precipitation and flood frequency. In addition, main findings of the analyses are presented, including a comparison of trend analysis results and climate change projections. Existing guidelines in Europe on design flood and design rainfall estimation that incorporate climate change are reviewed. The report concludes with a discussion of research needs on non-stationary frequency analysis for considering the effects of climate change and inclusion in design guidelines. Trend analyses are reported for 21 countries in Europe with results for extreme precipitation, extreme streamflow or both. A large number of national and regional trend studies have been carried out. Most studies are based on statistical methods applied to individual time series of extreme precipitation or extreme streamflow using the non-parametric Mann-Kendall trend test or regression analysis. Some studies have been reported that use field significance or regional consistency tests to analyse trends over larger areas. Some of the studies also include analysis of trend attribution. The studies reviewed indicate that there is some evidence of a general increase in extreme precipitation, whereas there are no clear indications of significant increasing trends at regional or national level of extreme streamflow. For some smaller regions increases in extreme streamflow are reported. Several studies from regions dominated by snowmelt-induced peak flows report decreases in extreme streamflow and earlier spring snowmelt peak flows. Climate change projections have been reported for 14 countries in Europe with results for extreme precipitation, extreme streamflow or both. The review shows various approaches for producing climate projections of extreme precipitation and flood frequency based on alternative climate forcing scenarios, climate projections from available global and regional climate models, methods for statistical downscaling and bias correction, and alternative hydrological models. A large number of the reported studies are based on an ensemble modelling approach that use several climate forcing scenarios and climate model projections in order to address the uncertainty on the projections of extreme precipitation and flood frequency. Some studies also include alternative statistical downscaling and bias correction methods and hydrological modelling approaches. Most studies reviewed indicate an increase in extreme precipitation under a future climate, which is consistent with the observed trend of extreme precipitation. Hydrological projections of peak flows and flood frequency show both positive and negative changes. Large increases in peak flows are reported for some catchments with rainfall-dominated peak flows, whereas a general decrease in flood magnitude and earlier spring floods are reported for catchments with snowmelt-dominated peak flows. The latter is consistent with the observed trends. The review of existing guidelines in Europe on design floods and design rainfalls shows that only few countries explicitly address climate change. These design guidelines are based on climate change adjustment factors to be applied to current design estimates and may depend on design return period and projection horizon. The review indicates a gap between the need for considering climate change impacts in design and actual published guidelines that incorporate climate change in extreme precipitation and flood frequency. Most of the studies reported are based on frequency analysis assuming stationary conditions in a certain time window (typically 30 years) representing current and future climate. There is a need for developing more consistent non-stationary frequency analysis methods that can account for the transient nature of a changing climate

In response to 'Celebrate citation: flipping the pedagogy of plagiarism in Qatar'

In her article (http://uobrep.openrepository.com/uobrep/handle/10547/335947) Molly McHarg makes several points that I agree with, particularly that for the majority of students the plagiarism is not deliberate but is due to a lack of understanding of how to reference correctly

Environmental Games and Queue Models

This paper considers a pollution and control game which uses a queuing framework. This framework allows an accounting of pollution events, environmental pollution quality and the application of controls to maintain a desirable quality of the environment. A number of examples are used to highlight the approach and demonstrates both its theoretical and practical usefulness.Environment; Control; Quality; Queuing

Vol. 52 No. 4 - Whole No. 375

Mythprint is the monthly bulletin of the Mythopoeic Society, a nonprofit educational organization devoted to the study, discussion, and enjoyment of myth and fantasy literature, especially the works of J.R.R. Tolkien, C.S. Lewis, and Charles Williams. To promote these interests, the Society publishes three magazines, maintains a World Wide Web site, and sponsors the annual Mythopoeic Conference and awards for fiction and scholarship, as well as local and written discussion groups

Fraud detection for online banking for scalable and distributed data

Online fraud causes billions of dollars in losses for banks. Therefore, online banking fraud detection is an important field of study. However, there are many challenges in conducting research in fraud detection. One of the constraints is due to unavailability of bank datasets for research or the required characteristics of the attributes of the data are not available. Numeric data usually provides better performance for machine learning algorithms. Most transaction data however have categorical, or nominal features as well. Moreover, some platforms such as Apache Spark only recognizes numeric data. So, there is a need to use techniques e.g. One-hot encoding (OHE) to transform categorical features to numerical features, however OHE has challenges including the sparseness of transformed data and that the distinct values of an attribute are not always known in advance. Efficient feature engineering can improve the algorithm’s performance but usually requires detailed domain knowledge to identify correct features. Techniques like Ripple Down Rules (RDR) are suitable for fraud detection because of their low maintenance and incremental learning features. However, high classification accuracy on mixed datasets, especially for scalable data is challenging. Evaluation of RDR on distributed platforms is also challenging as it is not available on these platforms. The thesis proposes the following solutions to these challenges: • We developed a technique Highly Correlated Rule Based Uniformly Distribution (HCRUD) to generate highly correlated rule-based uniformly-distributed synthetic data. • We developed a technique One-hot Encoded Extended Compact (OHE-EC) to transform categorical features to numeric features by compacting sparse-data even if all distinct values are unknown. • We developed a technique Feature Engineering and Compact Unified Expressions (FECUE) to improve model efficiency through feature engineering where the domain of the data is not known in advance. • A Unified Expression RDR fraud deduction technique (UE-RDR) for Big data has been proposed and evaluated on the Spark platform. Empirical tests were executed on multi-node Hadoop cluster using well-known classifiers on bank data, synthetic bank datasets and publicly available datasets from UCI repository. These evaluations demonstrated substantial improvements in terms of classification accuracy, ruleset compactness and execution speed.Doctor of Philosoph

Prudent fraud detection in internet banking

Most commercial Fraud Detection components of Internet banking systems use some kind of hybrid setup usually comprising a Rule-Base and an Artificial Neural Network. Such rule bases have been criticised for a lack of innovation in their approach to Knowledge Acquisition and maintenance. Furthermore, the systems are brittle; they have no way of knowing when a previously unseen set of fraud patterns is beyond their current knowledge. This limitation may have far reaching consequences in an online banking system. This paper presents a viable alternative to brittleness in Knowledge Based Systems; a potential milestone in the rapid detection of unique and novel fraud patterns in Internet banking. The experiments conducted with real online banking transaction log files suggest that Prudent based fraud detection may be a worthy alternative in online banking. © 2012 IEEE