Specifying Exposure Classification Parameters for Sensitivity Analysis: Family Breast Cancer History

One of the challenges to implementing sensitivity analysis for exposure misclassification is the process of specifying the classification proportions (eg, sensitivity and specificity). The specification of these assignments is guided by three sources of information: estimates from validation studies, expert judgment, and numerical constraints given the data. The purpose of this teaching paper is to describe the process of using validation data and expert judgment to adjust a breast cancer odds ratio for misclassification of family breast cancer history. The parameterization of various point estimates and prior distributions for sensitivity and specificity were guided by external validation data and expert judgment. We used both nonprobabilistic and probabilistic sensitivity analyses to investigate the dependence of the odds ratio estimate on the classification error. With our assumptions, a wider range of odds ratios adjusted for family breast cancer history misclassification resulted than portrayed in the conventional frequentist confidence interval.Children's Cancer Research Fund, Minneapolis, MN, US

Modality effects in implicit artificial grammar learning: An EEG study

Recently, it has been proposed that sequence learning engages a combination of modality-specific operating networks and modality-independent computational principles. In the present study, we compared the behavioural and EEG outcomes of implicit artificial grammar learning in the visual vs. auditory modality. We controlled for the influence of surface characteristics of sequences (Associative Chunk Strength), thus focusing on the strictly structural aspects of sequence learning, and we adapted the paradigms to compensate for known frailties of the visual modality compared to audition (temporal presentation, fast presentation rate). The behavioural outcomes were similar across modalities. Favouring the idea of modality-specificity, ERPs in response to grammar violations differed in topography and latency (earlier and more anterior component in the visual modality), and ERPs in response to surface features emerged only in the auditory modality. In favour of modality-independence, we observed three common functional properties in the late ERPs of the two grammars: both were free of interactions between structural and surface influences, both were more extended in a grammaticality classification test than in a preference classification test, and both correlated positively and strongly with theta event-related-synchronization during baseline testing. Our findings support the idea of modality-specificity combined with modality-independence, and suggest that memory for visual vs. auditory sequences may largely contribute to cross-modal differences. (C) 2018 Elsevier B.V. All rights reserved.Max Planck Institute for Psycholinguistics; Donders Institute for Brain, Cognition and Behaviour; Fundacao para a Ciencia e Tecnologia [PTDC/PSI-PC0/110734/2009, UID/BIM/04773/2013, CBMR 1334, PEst-OE/EQB/1A0023/2013, UM/PSI/00050/2013

Adversarial Detection of Flash Malware: Limitations and Open Issues

During the past four years, Flash malware has become one of the most insidious threats to detect, with almost 600 critical vulnerabilities targeting Adobe Flash disclosed in the wild. Research has shown that machine learning can be successfully used to detect Flash malware by leveraging static analysis to extract information from the structure of the file or its bytecode. However, the robustness of Flash malware detectors against well-crafted evasion attempts - also known as adversarial examples - has never been investigated. In this paper, we propose a security evaluation of a novel, representative Flash detector that embeds a combination of the prominent, static features employed by state-of-the-art tools. In particular, we discuss how to craft adversarial Flash malware examples, showing that it suffices to manipulate the corresponding source malware samples slightly to evade detection. We then empirically demonstrate that popular defense techniques proposed to mitigate evasion attempts, including re-training on adversarial examples, may not always be sufficient to ensure robustness. We argue that this occurs when the feature vectors extracted from adversarial examples become indistinguishable from those of benign data, meaning that the given feature representation is intrinsically vulnerable. In this respect, we are the first to formally define and quantitatively characterize this vulnerability, highlighting when an attack can be countered by solely improving the security of the learning algorithm, or when it requires also considering additional features. We conclude the paper by suggesting alternative research directions to improve the security of learning-based Flash malware detectors

From print to Web: issues in re-purposing for an Open Resources Repository

The Open Educational Resources (OER) movement has gained rapid support for its goals of universal access to education. The UK Open University's contribution is OpenLearn, a project funded by the William and Flora Hewlett Foundation which, over the next two years, aims to re-purpose several thousand study-hours of existing learning materials for online delivery. The UK Open University has gained a hard-won reputation for the quality of its learning materials and integrated Supported Open Learning model. However, these materials are, as a rule, developed within the framework of long courses that typically require between 300 and 600 hours of study. Furthermore, many of the courses are 'interdisciplinary' in that they are developed by teams that include members associated with different faculties. The courses are, therefore, generally structured in terms of themes that run throughout the course and may span a variety of academic disciplines. Also, despite the breadth of knowledge brought into play when a course is developed, the courses normally reflect pedagogical and disciplinary assumptions and views that are prevalent in the UK. Based on the authors' experience of the OpenLearn project, this paper explores some of the key issues encountered when re-purposing resources. These issues include how to provide material not supported by tutorial guidance, the suitability of media components for conversion and the inter-relationship between the multimedia components. The paper will also briefly discuss the requirements for evaluation of the re-purposing process. The issues raised are potentially of relevance to other re-purposing initiatives