Machine Learning and Survey-based Predictors of InfoSec Non-Compliance

Survey items developed in behavioral Information Security (InfoSec) research should be practically useful in identifying individuals who are likely to create risk by failing to comply with InfoSec guidance. The literature shows that attitudes, beliefs, and perceptions drive compliance behavior and has influenced the creation of a multitude of training programs focused on improving ones’ InfoSec behaviors. While automated controls and directly observable technical indicators are generally preferred by InfoSec practitioners, difficult-to-monitor user actions can still compromise the effectiveness of automatic controls. For example, despite prohibition, doubtful or skeptical employees often increase organizational risk by using the same password to authenticate corporate and external services. Analysis of network traffic or device configurations is unlikely to provide evidence of these vulnerabilities but responses to well-designed surveys might. Guided by the relatively new IPAM model, this study administered 96 survey items from the Behavioral InfoSec literature, across three separate points in time, to 217 respondents. Using systematic feature selection techniques, manageable subsets of 29, 20, and 15 items were identified and tested as predictors of non-compliance with security policy. The feature selection process validates IPAM's innovation in using nuanced self-efficacy and planning items across multiple time frames. Prediction models were trained using several ML algorithms. Practically useful levels of prediction accuracy were achieved with, for example, ensemble tree models identifying 69% of the riskiest individuals within the top 25% of the sample. The findings indicate the usefulness of psychometric items from the behavioral InfoSec in guiding training programs and other cybersecurity control activities and demonstrate that they are promising as additional inputs to AI models that monitor networks for security events.

Multimodal Spatio-Temporal Prediction with Stochastic Adversarial Networks

Spatio-temporal (ST) data is a collection of multiple time series data with different spatial locations and is inherently stochastic and unpredictable. An accurate prediction over such data is an important building block for several urban applications, such as taxi demand prediction, traffic flow prediction, and so on. Existing deep learning based approaches assume that outcome is deterministic and there is only one plausible future; therefore, cannot capture the multimodal nature of future contents and dynamics. In addition, existing approaches learn spatial and temporal data separately as they assume weak correlation between them. To handle these issues, in this article, we propose a stochastic spatio-temporal generative model (named D-GAN) which adopts Generative Adversarial Networks (GANs)-based structure for more accurate ST prediction in multiple time steps. D-GAN consists of two components: (1) spatio-temporal correlation network which models spatio-temporal joint distribution of pixels and supports a stochastic sampling of latent variables for multiple plausible futures; (2) a stochastic adversarial network to jointly learn generation and variational inference of data through implicit distribution modeling. D-GAN also supports fusion of external factors through explicit objective to improve the model learning. Extensive experiments performed on two real-world datasets show that D-GAN achieves significant improvements and outperforms baseline models.

Nonlinear Dynamics of a Single-Mode Semiconductor Laser with Long Delayed Optical Feedback: A Modern Experimental Characterization Approach

Semiconductor lasers can exhibit complex dynamical behavior in the presence of external perturbations. Delayed optical feedback, re-injecting part of the emitted light back into the laser cavity, in particular, can destabilize the laser’s emission. We focus on the emission properties of a semiconductor laser subject to such optical feedback, where the delay of the light re-injection is large compared to the relaxation oscillations period. We present an overview of the main dynamical features that emerge in semiconductor lasers subject to delayed optical feedback, emphasizing how to experimentally characterize these features using intensity and high-resolution optical spectra measurements. The characterization of the system requires the experimentalist to be able to simultaneously measure multiple time scales that can be up to six orders of magnitude apart, from the picosecond to the microsecond range. We highlight some experimental observations that are particularly interesting from the fundamental point of view and, moreover, provide opportunities for future photonic applications.

What could we do differently next time? Australian parents’ experiences of the short-term and long-term impacts of home schooling during the COVID-19 pandemic

Background COVID-19 lockdowns have resulted in school closures worldwide, requiring curriculum to be delivered to children remotely (home schooling). Qualitative evidence is needed to provide important context to the positive and negative impacts of home schooling and inform strategies to support caregivers and children as the pandemic continues. This study aimed to explore the experiences of home schooling caregivers at multiple time-points during the pandemic. Methods Data were obtained from a longitudinal survey of a representative Australian sample conducted over 8 waves during 2020 and 2021. Participants who had home schooled at least one child during COVID-19 completed open-ended questions at Wave 4 (May 2020; n = 176), Wave 7 (June 2020; n = 145), and Wave 8 (March 2021; n = 57). Participants were asked to describe what they found positive and challenging about home schooling (Wave 4), what they would do differently if they home schooled their children again (Wave 7), and the longer-term impacts of home schooling on caregivers and children (Wave 8). Results 91% of participants at Wave 4 reported at least one positive and/or negative aspect of home schooling. At Wave 8, 32% and 29% of participants reported no long-term positive or negative impacts of home schooling respectively. Using a qualitative content analysis approach, six themes were developed from the data, encompassing the impacts of home schooling on parents, and the perceived impacts on children. Impacts on parents included connecting with children, managing the work-life-school balance, and the challenge of home schooling when parents are not teachers. Perceived impacts on children included: quieter and safer learning at home, and the negatives of managing schoolwork load and social isolation. At Wave 7, 56 participants (44%) identified at least one thing they would do differently. Conclusions Despite some participants reporting positive experiences associated with home schooling, it remains challenging for many parents and their children. Supports for parents and children engaged in home schooling should provide clear and flexible guidance on how to balance schoolwork with other competing demands, assist parents who lack confidence in supporting their children’s remote learning, and address risks associated with social isolation.

Characteristics of the gut microbiome in women with gestational diabetes mellitus: A systematic review

Background The incidence of women developing gestational diabetes mellitus (GDM) is increasing, which is associated with an increased risk of type 2 diabetes mellitus (T2DM) for both mother and child. Gut microbiota dysbiosis may contribute to the pathogenesis of both GDM and the accompanying risk of T2DM. Thus, a better understanding of the microbial communities associated with GDM could offer a potential target for intervention and treatment in the future. Therefore, we performed a systematic review to investigate if the GDM women have a distinct gut microbiota composition compared to non-GDM women. Methods We identified 21 studies in a systematic literature search of Embase and PubMed up to February 24, 2021. Data on demographics, methodology and identified microbial metrics were extracted. The quality of each study was assessed according to the Newcastle-Ottawa Scale. Results Sixteen of the studies did find a GDM-associated gut microbiota, although no consistency could be seen. Only Collinsella and Blautia showed a tendency to be increased in GDM women, whereas the remaining genera were significantly different in opposing directions. Conclusion Although most of the studies found an association between GDM and gut microbiota dysbiosis, no overall GDM-specific gut microbiota could be identified. All studies in the second trimester found a difference between GDM and non-GDM women, indicating that dysbiosis is present at the time of diagnosis. Nevertheless, it is still unclear when the dysbiosis develops, as no consensus could be seen between the studies investigating the gut microbiota in the first trimester of pregnancy. However, studies varied widely concerning methodology and study design, which might explain the highly heterogeneous gut microbiota compositions between studies. Therefore, future studies need to include multiple time points and consider possible confounding factors such as ethnicity, pre-pregnancy body mass index, and GDM treatment.

Usefulness of circulating tumor DNA by targeting human papilloma virus-derived sequences as a biomarker in p16-positive oropharyngeal cancer

In head and neck cancer, early detection of recurrence after treatment is important. The contemporary development of therapeutic agents have improved the prognosis after recurrence; however, no biomarker has been established for evaluating therapeutic effects or detecting recurrence. Recently, circulating tumor DNA (ctDNA), which comprises DNA derived from tumor cells and exists in the form of cell-free DNA in the blood, has attracted attention as a minimally invasive and repeatable biomarker for detecting cancer. We validated the usefulness of ctDNA of human papilloma virus (HPV)-derived sequences as a biomarker in HPV-related p16-positive oropharyngeal cancer by assessing 25 patients with p16-positive oropharyngeal cancer. Blood samples were collected from each patient at multiple time points during the treatment, and the plasma was preserved. The ctDNA was extracted from the plasma and analyzed using digital polymerase chain reaction. HPV-derived ctDNA was detected in 14 (56%) of the 25 patients. In all the patients, the samples were found to be ctDNA-negative after initial treatment. Cancer recurrence was observed in 2 of the 14 patients; HPV-derived ctDNA was detected at the time of recurrence. Our results indicate that HPV-derived ctDNA can be a prospective biomarker for predicting the recurrence of p16-positive oropharyngeal cancer.

On the rigidity of four hundred Pickering-stabilised microbubbles

This study explores the rigidity of Pickering-stabilised microbubbles subjected to low-amplitude ultrasound. Such microbubbles might be suitable ultrasound contrast agents. Using an adapted Rayleigh-Plesset equation, we modelled the dynamics of microbubbles with a 7.6-N m−1 shell stiffness under 1-MHz, 0.2-MPa sonication. Such dynamics were observed experimentally, too, using high-speed photography. The maximum expansions were agreeing with those predicted for Pickering-stabilised microbubbles. Subjecting microbubbles to multiple time- delayed pulses yielded the same result. We conclude that Pickering-stabilised microbubbles remain very stable at low acoustic amplitudes.