Investigating Facebook groups through a random graph model

Facebook disseminates messages for billions of users everyday. Though there are log files stored on central servers, law enforcement agencies outside of the U.S. cannot easily acquire server log files from Facebook. This work models Facebook user groups by using a random graph model. Our aim is to facilitate detectives quickly estimating the size of a Facebook group with which a suspect is involved. We estimate this group size according to the number of immediate friends and the number of extended friends which are usually accessible by the public. We plot and examine UML diagrams to describe Facebook functions. Our experimental results show that asymmetric Facebook friendship fulfills the assumption of applying random graph models.

Detection on application layer DDoS using random walk model

Application Layer Distributed Denial of Service (ALDDoS) attacks have been increasing rapidly with the growth of Botnets and Ubiquitous computing. Differentiate to the former DDoS attacks, ALDDoS attacks cannot be efficiently detected, as attackers always adopt legitimate requests with real IP address, and the traffic has high similarity to legitimate traffic. In spite of that, we think, the attackers' browsing behavior will have great disparity from that of the legitimate users'. In this paper, we put forward a novel user behavior-based method to detect the application layer asymmetric DDoS attack. We introduce an extended random walk model to describe user browsing behavior and establish the legitimate pattern of browsing sequences. For each incoming browser, we observe his page request sequence and predict subsequent page request sequence based on random walk model. The similarity between the predicted and the observed page request sequence is used as a criterion to measure the legality of the user, and then attacker would be detected based on it. Evaluation results based on real collected data set has demonstrated that our method is very effective in detecting asymmetric ALDDoS attacks. © 2014 IEEE.

Monetary models of exchange rates and the random walk

Three alternative monetary models of exchange rate are tested using data on the Italian lira - US doIIar exchange rate. II is shown that up to the early 1990s these economic models perform better than the random walk model in out-of-sample forecasts.

A model of the Australia-US exchange rate

This paper develops a model of exchange rate determination within an error correction framework. The intention is to identify both long and short term determinants that can be used to forecast the AUD/US exchange rate. The paper identifies a set of significant variables associated with exchange rate movements over a twenty year period from 1984 to 2004. Specifically, the overnight interest rate differential, Australia's foreign trade-weighted exposure to commodity prices as well as exchange rate volatility are variables identified that are able explain movements in the AUDIUS dollar relationship. An error correction model is subsequently constructed that incorporates an equilibrium correction term, a short-term interest rate differential variable, a commodity price variable and a proxy for exchange rate volatility. The model is then used to forecast out of sample and is found to dominate a naIve random walk model based on three different metrics.

The influence of parents, siblings and peers on pre- and early-teen smoking : a multilevel model

Introduction and Aims. Despite considerable success in tobacco control, many teenagers in Australia and other industrialised countries still smoke tobacco. There is mixed evidence on the relative influence of proximal social networks (parents/siblings/peers) on pre- and early-teen smoking, and no research has examined how these influences compare after accounting for school- and community-level effects.The aim of this study was to compare the relative influences of parents, siblings and peers, after accounting for school- and community-level variation in smoking.

Design and Methods. A cross-sectional fixed and random effects model of smoking prevalence was used, with individuals (n = 7314) nested within schools (n = 231) nested within communities (n = 30). Grade 6 and 8 students (modal ages 11 and 13 years) completed an on-line survey. Key variables included parent/sibling/peer use. Controls included alcohol involvement, sensation seeking, pro-social beliefs, laws/norms about substance use and school commitment.

Results.There was significant variation in smoking at both the school and community levels, supporting the need for a multilevel model. Individual-level predictors accounted for much of the variance at higher levels. The strongest effects were for number of friends who smoke, sibling smoking and alcohol involvement. Smaller significant effects were found for parent smoking. At the community level, socioeconomic disadvantage was significant, but community-level variance in pro-social and drug-related laws/norms was not related to smoking.

Discussion and Conclusions. Cross-level interactions were generally non-significant. Early teenage smoking was best explained by sibling and peer smoking, and individual risks largely accounted for the substantial variation observed across schools and communities. In terms of future tobacco control, findings point to the utility of targeting families in disadvantaged communities.[Kelly AB, O'Flaherty M, Connor JP, Homel R, Toumbourou JW, Patton GC, Williams J. The influence of parents, siblings and peers on pre- and early-teen smoking: A multilevel model.

Certificate-based signature : security model and efficient construction

In Eurocrypt 2003, Gentry introduced the notion of certificate-based encryption. The merit of certificate-based encryption lies in the following features: (1) providing more efficient public-key infrastructure (PKI) that requires less infrastructure, (2) solving the certificate revocation problem, and (3) eliminating third-party queries in the traditional PKI. In addition, it also solves the inherent key escrow problem in the identity-based cryptography. In this paper, we first introduce a new attack called the “Key Replacement Attack” in the certificate-based system and refine the security model of certificate-based signature. We show that the certificate-based signature scheme presented by Kang, Park and Hahn in CT-RSA 2004 is insecure against key replacement attacks. We then propose a new certificate-based signature scheme, which is shown to be existentially unforgeable against adaptive chosen message attacks under the computational Diffie-Hellman assumption in the random oracle model. Compared with the certificate-based signature scheme in CT-RSA 2004, our scheme enjoys shorter signature length and less operation cost, and hence, our scheme outperforms the existing schemes in the literature.

Towards a BIM-based energy rating system

Governments in Australia are faced with policy implementation that mandates higher energy efficient housing (Foran, Lenzen & Dey 2005). To this effect, the National Construction Code (NCC) 2013 stipulates the minimum energy performance for residential buildings as 114MJ/m2 per annum or 6 stars on an energy rating scale. Compliance with this minimum is mandatory but there are several methods through which residential buildings can be rated to comply with the deemed to satisfy provisions outlined in the NCC. FirstRate5 is by far the most commonly used simulation software used in Victoria, Australia. Meanwhile, Building Information Modelling (BIM), using software such as ArchiCAD has gained a foothold in the industry. The energy simulation software within ArchiCAD, EcoDesigner, enables the reporting on the energy performance based on BIM elements that contain thermal information. This research is founded on a comparative study between FirstRate5 and EcoDesigner. Three building types were analysed and compared. The comparison finds significant differences between simulations, being, measured areas, thermal loads and potentially serious shortcomings within FirstRate5, that are discussed along with the future potential of a fully BIM-integrated model for energy rating certification in Victoria. © 2014, The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong.

Flight altitude of trans-Sahara migrants in autumn: a comparison of radar observations with predictions from meteorological conditions and water and energy balance models

Radar observations on the altitude of bird migration and altitudinal profiles of meteorological conditions over the Sahara desert are presented for the autumn migratory period. Migratory birds fly at an average altitude of 1016 m (a.s.l.) during the day and 571 m during the night. Weather data served to calculate flight range using two models: an energy model (EM) and an energy-and-water model (EWM). The EM assumes that fuel supply limits flight range whereas the EWM assumes that both fuel and water may limit flight range. Flight ranges estimated with the EM were generally longer than those with the EWM. This indicates that trans-Sahara migrants might have more problems balancing their water than their energy budget. However, if we assume fuel stores to consist of 70% instead of 100% fat (the remainder consisting of 9% protein and 21% water), predicted flight ranges of the EM and EWM largely overlap. Increased oxygen extraction, reduced flight costs, reduced exhaled air temperature, reduced cutaneous water loss and increased tolerance to water loss are potential physiological adaptations that would improve the water budget in migrants. Both the EM and EWM predict optimal flight altitudes in agreement with radar observations in autumn. Optimal flight altitudes are differently predicted by the EM and EWM for nocturnal spring migration. During spring, the EWM predicts moderately higher and the EM substantially higher flight altitudes than during autumn. EWM predictions are therefore in better agreement with radar observations on flight altitude of migrants over the Negev desert in spring than EM predictions.

A graph-based model for context-aware recommendation using implicit feedback data

Recommender systems have been successfully dealing with the problem of information overload. However, most recommendation methods suit to the scenarios where explicit feedback, e.g. ratings, are available, but might not be suitable for the most common scenarios with only implicit feedback. In addition, most existing methods only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a graph-based generic recommendation framework, which constructs a Multi-Layer Context Graph (MLCG) from implicit feedback data, and then performs ranking algorithms in MLCG for context-aware recommendation. Specifically, MLCG incorporates a variety of contextual information into a recommendation process and models the interactions between users and items. Moreover, based on MLCG, two novel ranking methods are developed: Context-aware Personalized Random Walk (CPRW) captures user preferences and current situations, and Semantic Path-based Random Walk (SPRW) incorporates semantics of paths in MLCG into random walk model for recommendation. The experiments on two real-world datasets demonstrate the effectiveness of our approach.

The exchange rate disconnect puzzle: a resolution?

Early empirical studies of exchange rate determinants demonstrated that fundamentals-based monetary models were unable to outperform the benchmark random walk model in out-of-sample forecasts while later papers found evidence in favor of long-run exchange rate predictability. More recent theoretical works have adopted a microeconomic structure; a utility-based new open economy macroeconomic framework and a rational expectations present value model. Some recent empirical work argues that if the models are adjusted for parameter instability, it is a good predictor of nominal exchange rates while others use aggregate idiosyncratic volatility to generate good predictions. This latest research supports the idea that fundamental economic variables are likely to influence exchange rates especially in the long run and further that the emphasis should change to the economic value or utility based value to assess these macroeconomic models.

Use of calcium or calcuim in combination with vitamin D supplementation to prevent fractures and bone loss in people aged 50 years and older: a meta-analysis

Background:  Whether calcium supplementation can reduce osteoporotic fractures is uncertain. We did a meta-analysis to include all the randomised trials in which calcium, or calcium in combination with vitamin D, was used to prevent fracture and osteoporotic bone loss.

Methods:  We identified 29 randomised trials (n=63 897) using electronic databases, supplemented by a hand-search of reference lists, review articles, and conference abstracts. All randomised trials that recruited people aged 50 years or older were eligible. The main outcomes were fractures of all types and percentage change of bone-mineral density from baseline. Data were pooled by use of a random-effect model.

Findings:  In trials that reported fracture as an outcome (17 trials, n=52 625), treatment was associated with a 12% risk reduction in fractures of all types (risk ratio 0·88, 95% CI 0·83–0·95; p=0·0004). In trials that reported bone-mineral density as an outcome (23 trials, n=41 419), the treatment was associated with a reduced rate of bone loss of 0·54% (0·35–0·73; p<0·0001) at the hip and 1·19% (0·76–1·61%; p<0·0001) in the spine. The fracture risk reduction was significantly greater (24%) in trials in which the compliance rate was high (p<0·0001). The treatment effect was better with calcium doses of 1200 mg or more than with doses less than 1200 mg (0·80 vs 0·94; p=0·006), and with vitamin D doses of 800 IU or more than with doses less than 800 IU (0·84 vs 0·87; p=0·03).

Interpretation:  Evidence supports the use of calcium, or calcium in combination with vitamin D supplementation, in the preventive treatment of osteoporosis in people aged 50 years or older. For best therapeutic effect, we recommend minimum doses of 1200 mg of calcium, and 800 IU of vitamin D (for combined calcium plus vitamin D supplementation).

Concepts and dimensionality in modeling unsaturated water flow and solute transport

Many environmental studies require accurate simulation of water and solute fluxes in the unsaturated zone. This paper evaluates one- and multi-dimensional approaches for soil water flow as well as different spreading mechanisms to model solute behavior at different scales. For quantification of soil water fluxes,Richards equation has become the standard. Although current numerical codes show perfect water balances, the calculated soil water fluxes in case of head boundary conditions may depend largely on the method used for spatial averaging of the hydraulic conductivity. Atmospheric boundary conditions, especially in the case of phreatic groundwater levels fluctuating above and below a soil surface, require sophisticated solutions to ensure convergence. Concepts for flow in soils with macro pores and unstable wetting fronts are still in development. One-dimensional flow models are formulated to work with lumped parameters in order to account for the soil heterogeneity and preferential flow. They can be used at temporal and spatial scales that are of interest to water managers and policymakers. Multi-dimensional flow models are hampered by data and computation requirements.Their main strength is detailed analysis of typical multi-dimensional flow problems, including soil heterogeneity and preferential flow. Three physically based solute-transport concepts have been proposed to describe solute spreading during unsaturated flow: The stochastic-convective model (SCM), the convection-dispersion equation (CDE), and the fraction aladvection-dispersion equation (FADE). A less physical concept is the continuous-time random-walk process (CTRW). Of these, the SCM and the CDE are well established, and their strengths and weaknesses are identified. The FADE and the CTRW are more recent,and only a tentative strength weakness opportunity threat (SWOT)analysis can be presented at this time. We discuss the effect of the number of dimensions in a numerical model and the spacing between model nodes on solute spreading and the values of the solute-spreading parameters. In order to meet the increasing complexity of environmental problems, two approaches of model combination are used: Model integration and model coupling. Amain drawback of model integration is the complexity of there sulting code. Model coupling requires a systematic physical domain and model communication analysis. The setup and maintenance of a hydrologic framework for model coupling requires substantial resources, but on the other hand, contributions can be made by many research groups.

Sequential fixed-width confidence bands for kernel regression estimation

We consider a random design model based on independent and identically distributed (iid) pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1−. Here, d(> 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths.

The sample size was optimized using the purely and two-stage sequential procedure together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confidence bands based on the local linear estimator have the best performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.

Sample size determination for kernel regression estimation using sequential fixed-width confidence bands

We consider a random design model based on independent and identically distributed pairs of observations (Xi, Yi), where the regression function m(x) is given by m(x) = E(Yi|Xi = x) with one independent variable. In a nonparametric setting the aim is to produce a reasonable approximation to the unknown function m(x) when we have no precise information about the form of the true density, f(x) of X. We describe an estimation procedure of non-parametric regression model at a given point by some appropriately constructed fixed-width (2d) confidence interval with the confidence coefficient of at least 1−. Here, d(> 0) and 2 (0, 1) are two preassigned values. Fixed-width confidence intervals are developed using both Nadaraya-Watson and local linear kernel estimators of nonparametric regression with data-driven bandwidths. The sample size was optimized using the purely and two-stage sequential procedures together with asymptotic properties of the Nadaraya-Watson and local linear estimators. A large scale simulation study was performed to compare their coverage accuracy. The numerical results indicate that the confi dence bands based on the local linear estimator have the better performance than those constructed by using Nadaraya-Watson estimator. However both estimators are shown to have asymptotically correct coverage properties.