Internet-delivered cognitive control training as a preventive intervention for remitted depressed patients : protocol for a randomized controlled trial

Background: Preventing recurrence of depression forms an important challenge for current treatments. Cognitive control impairments often remain present during remission of depression, putting remitted depressed patients at heightened risk for new depressive episodes by disrupting emotion regulation processes. Importantly, research indicates that cognitive control training targeting working memory functioning shows potential in reducing maladaptive emotion regulation and depressive symptomatology in clinically depressed patients and at-risk student samples. The current study aims to test the effectiveness of cognitive control training as a preventive intervention in a remitted depressed sample, exploring effects of cognitive control training on rumination and depressive symptomatology, along with indicators of adaptive emotion regulation and functioning. Methods/design: We present a double blind randomized controlled design. Remitted depressed adults will complete 10 online sessions of a cognitive control training targeting working memory functioning or a low cognitive load training (active control condition) over a period of 14 days. Effects of training on primary outcome measures of rumination and depressive symptomatology will be assessed pre-post training and at three months follow-up, along with secondary outcome measure adaptive emotion regulation. Long-term effects of cognitive control training on broader indicators of functioning will be assessed at three months follow-up (secondary outcome measures). Discussion: This study will provide information about the effectiveness of cognitive control training for remitted depressed adults in reducing vulnerability for depression. Furthermore, this study will address key questions concerning the mechanisms underlying the effects of cognitive control training, will take into account the subjective experience of the patients (including a self-report measure for cognitive functioning), and explore whether these effects extend to broad measures of functioning such as Quality of Life and disability

Hyp3rArmor: reducing web application exposure to automated attacks

Web applications (webapps) are subjected constantly to automated, opportunistic attacks from autonomous robots (bots) engaged in reconnaissance to discover victims that may be vulnerable to specific exploits. This is a typical behavior found in botnet recruitment, worm propagation, largescale fingerprinting and vulnerability scanners. Most anti-bot techniques are deployed at the application layer, thus leaving the network stack of the webapp’s server exposed. In this paper we present a mechanism called Hyp3rArmor, that addresses this vulnerability by minimizing the webapp’s attack surface exposed to automated opportunistic attackers, for JavaScriptenabled web browser clients. Our solution uses port knocking to eliminate the webapp’s visible network footprint. Clients of the webapp are directed to a visible static web server to obtain JavaScript that authenticates the client to the webapp server (using port knocking) before making any requests to the webapp. Our implementation of Hyp3rArmor, which is compatible with all webapp architectures, has been deployed and used to defend single and multi-page websites on the Internet for 114 days. During this time period the static web server observed 964 attempted attacks that were deflected from the webapp, which was only accessed by authenticated clients. Our evaluation shows that in most cases client-side overheads were negligible and that server-side overheads were minimal. Hyp3rArmor is ideal for critical systems and legacy applications that must be accessible on the Internet. Additionally Hyp3rArmor is composable with other security tools, adding an additional layer to a defense in depth approach.This work has been supported by the National Science Foundation (NSF) awards #1430145, #1414119, and #1012798

Adding Salt to Pepper: A Structured Security Assessment over a Humanoid Robot

The rise of connectivity, digitalization, robotics, and artificial intelligence (AI) is rapidly changing our society and shaping its future development. During this technological and societal revolution, security has been persistently neglected, yet a hacked robot can act as an insider threat in organizations, industries, public spaces, and private homes. In this paper, we perform a structured security assessment of Pepper, a commercial humanoid robot. Our analysis, composed by an automated and a manual part, points out a relevant number of security flaws that can be used to take over and command the robot. Furthermore, we suggest how these issues could be fixed, thus, avoided in the future. The very final aim of this work is to push the rise of the security level of IoT products before they are sold on the public market.Comment: 8 pages, 3 figures, 4 table

Mining Fix Patterns for FindBugs Violations

In this paper, we first collect and track a large number of fixed and unfixed violations across revisions of software. The empirical analyses reveal that there are discrepancies in the distributions of violations that are detected and those that are fixed, in terms of occurrences, spread and categories, which can provide insights into prioritizing violations. To automatically identify patterns in violations and their fixes, we propose an approach that utilizes convolutional neural networks to learn features and clustering to regroup similar instances. We then evaluate the usefulness of the identified fix patterns by applying them to unfixed violations. The results show that developers will accept and merge a majority (69/116) of fixes generated from the inferred fix patterns. It is also noteworthy that the yielded patterns are applicable to four real bugs in the Defects4J major benchmark for software testing and automated repair.Comment: Accepted for IEEE Transactions on Software Engineerin

APOE genotype and entorhinal cortex volume in non-demented community-dwelling adults in midlife and early old age

Copyright © 2012 IOS PressThis article has been made available through the Brunel Open Access Publishing Fund.The apolipoprotein E (APOE) ε4 allele is a risk factor for the neuropathological decline accompanying Alzheimer's disease (AD) while, conversely, the ε2 allele offers protection. One of the brain structures exhibiting the earliest changes associated with the disease is the entorhinal cortex. We therefore investigated the volumes of the entorhinal cortex and other structures in the medial temporal lobe including the parahippocampal gyrus, temporal pole, and inferior, middle, and superior temporal cortices, in relation to APOE genotype. Our main objectives were to determine if (a) volumes systematically varied according to allele in a stepwise fashion, ε2 > ε3 > ε4, and (b) associations varied according to age. We investigate this association in 627 non-demented community-dwelling adults in middle age (44 to 48 years; n = 314) and older age (64 to 68 years; n = 313) who underwent structural MRI scans. We found no evidence of APOE-related variation in brain volumes in the age groups examined. We conclude that if a ε2 > ε3 > ε4 pattern in brain volumes does emerge in non-demented adults living in the community in old age, it is not until after the age of 68 years.This study was funded by the UK Leverhulme Trust, the British Academy, the NHMRC Research Fellowship No. 471501, the NHMRC Research Fellowship No.#1002560, the National Health and Medical Research Council of Australia Unit Grant No. 973302, Program Grant No. 179805, Project grant No. 157125; Program grant no. 350833, and the National Computational Infrastructure. This article is made available through the Brunel Open Access Publishing Fund