No. 07: Household Food Security and Access to Medical Care in Maputo, Mozambique

The relationship between household access to medical care and food security is a potentially circuitous and challenging relationship to model. This discussion paper uses multiple modelling techniques to determine the quality of the relationships between these variables using household survey data collected by the Hungry Cities Partnership in 2014 in Maputo, Mozambique. The results of the investigation are framed according to the Sustainable Livelihood Framework and indicate a predictive relationship between household food security status and consistent household medical care access among the sampled households. The results also identify potential conditional independence in the relationship between other demographic variables and these two dependent variables among the surveyed households

Visual and Contextual Modeling for the Detection of Repeated Mild Traumatic Brain Injury.

Currently, there is a lack of computational methods for the evaluation of mild traumatic brain injury (mTBI) from magnetic resonance imaging (MRI). Further, the development of automated analyses has been hindered by the subtle nature of mTBI abnormalities, which appear as low contrast MR regions. This paper proposes an approach that is able to detect mTBI lesions by combining both the high-level context and low-level visual information. The contextual model estimates the progression of the disease using subject information, such as the time since injury and the knowledge about the location of mTBI. The visual model utilizes texture features in MRI along with a probabilistic support vector machine to maximize the discrimination in unimodal MR images. These two models are fused to obtain a final estimate of the locations of the mTBI lesion. The models are tested using a novel rodent model of repeated mTBI dataset. The experimental results demonstrate that the fusion of both contextual and visual textural features outperforms other state-of-the-art approaches. Clinically, our approach has the potential to benefit both clinicians by speeding diagnosis and patients by improving clinical care

An Optimal Game Theoretical Framework for Mobility Aware Routing in Mobile Ad hoc Networks

Selfish behaviors are common in self-organized Mobile Ad hoc Networks (MANETs) where nodes belong to different authorities. Since cooperation of nodes is essential for routing protocols, various methods have been proposed to stimulate cooperation among selfish nodes. In order to provide sufficient incentives, most of these methods pay nodes a premium over their actual costs of participation. However, they lead to considerably large overpayments. Moreover, existing methods ignore mobility of nodes, for simplicity. However, owing to the mobile nature of MANETs, this assumption seems unrealistic. In this paper, we propose an optimal game theoretical framework to ensure the proper cooperation in mobility aware routing for MANETs. The proposed method is based on the multi-dimensional optimal auctions which allows us to consider path durations, in addition to the route costs. Path duration is a metric that best reflects changes in topology caused by mobility of nodes and, it is widely used in mobility aware routing protocols. Furthermore, the proposed mechanism is optimal in that it minimizes the total expected payments. We provide theoretical analysis to support our claims. In addition, simulation results show significant improvements in terms of payments compared to the most popular existing methods

Multilayer Aggregation with Statistical Validation: Application to Investor Networks

Multilayer networks are attracting growing attention in many fields, including finance. In this paper, we develop a new tractable procedure for multilayer aggregation based on statistical validation, which we apply to investor networks. Moreover, we propose two other improvements to their analysis: transaction bootstrapping and investor categorization. The aggregation procedure can be used to integrate security-wise and time-wise information about investor trading networks, but it is not limited to finance. In fact, it can be used for different applications, such as gene, transportation, and social networks, were they inferred or observable. Additionally, in the investor network inference, we use transaction bootstrapping for better statistical validation. Investor categorization allows for constant size networks and having more observations for each node, which is important in the inference especially for less liquid securities. Furthermore, we observe that the window size used for averaging has a substantial effect on the number of inferred relationships. We apply this procedure by analyzing a unique data set of Finnish shareholders during the period 2004-2009. We find that households in the capital have high centrality in investor networks, which, under the theory of information channels in investor networks suggests that they are well-informed investors

Applications of Machine Learning to Threat Intelligence, Intrusion Detection and Malware

Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies with applications to many fields. This paper is a survey of use cases of ML for threat intelligence, intrusion detection, and malware analysis and detection. Threat intelligence, especially attack attribution, can benefit from the use of ML classification. False positives from rule-based intrusion detection systems can be reduced with the use of ML models. Malware analysis and classification can be made easier by developing ML frameworks to distill similarities between the malicious programs. Adversarial machine learning will also be discussed, because while ML can be used to solve problems or reduce analyst workload, it also introduces new attack surfaces

No. 15: The Food Security Implications of Gendered Access to Education and Employment in Maputo

The multiple linkages between gender and household food security in cities have been observed in diverse settings, at multiple scales, and through a variety of disciplinary lenses. The Hungry Cities Partnership is rooted in the importance of inclusive growth of cities, which includes a fundamental concern with genderbased injustices that reduce inclusivity, sustainability and food security by underpinning structural poverty. This discussion paper is motivated by the gap in policy-ready quantitative data needed to identify the ways in which gender inequality, food insecurity, and public policy are interconnected. Analysis of the 2014 survey of household food security in Maputo identified female headship as a household attribute closely associated with food insecurity and yet the employment and education status of the head largely mitigated the effect of female headship on food security. Using household survey data, this investigation defines the extent to which the relationship between the sex of the household head and food insecurity appears to be conditionally dependent upon employment and education. The findings provide further impetus to urban policy makers to operationalize gender-equality goals. For Hungry Cities researchers, it provides a model for gender-based analysis of household food security in other cities