More is Less: Perfectly Secure Oblivious Algorithms in the Multi-Server Setting

The problem of Oblivious RAM (ORAM) has traditionally been studied in a single-server setting, but more recently the multi-server setting has also been considered. Yet it is still unclear whether the multi-server setting has any inherent advantages, e.g., whether the multi-server setting can be used to achieve stronger security goals or provably better efficiency than is possible in the single-server case. In this work, we construct a perfectly secure 3-server ORAM scheme that outperforms the best known single-server scheme by a logarithmic factor. In the process, we also show, for the first time, that there exist specific algorithms for which multiple servers can overcome known lower bounds in the single-server setting.Comment: 36 pages, Accepted in Asiacrypt 201

Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms

Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well-known ground motion intensity measures, are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial ground motion sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology is deployed in a user-friendly web application.Keywords: seismic sequence; machine learning algorithms; repeated earthquakes; structural damage prediction; intensity measures; damage accumulation; machine learning; artificial neural networ

Structural Damage Prediction Under Seismic Sequence Using Neural Networks

Advanced machine learning algorithms, such as neural networks, have the potential to be successfully applied to many areas of system modelling. Several studies have been already conducted on forecasting structural damage due to individual earthquakes, ignoring the influence of seismic sequences, using neural networks. In the present study, an ensemble neural network approach is applied to predict the final structural damage of an 8-storey reinforced concrete frame under real and artificial ground motion sequences. Successive earthquakes consisted of two seismic events are utilised. We considered 16 well-known ground motion intensity measures and the structural damage that occurred by the first earthquake as the features of the machine-learning problem, while the final structural damage was the target. After the first seismic events and after the seismic sequences, both actual values of damage indices are calculated through nonlinear time history analysis. The machine-learning model is trained using the dataset generated from artificial sequences. Finally, the predictive capacity of the fitted neural network is accessed using the natural seismic sequences as a test set

Investigating the Monetary Policy of Central Banks with Assessment Indicators

This paper outlines a new method for using qualitative information to analyze the monetary policy strategy of central banks. Quantitative assessment indicators that are extracted from a central bank's public statements via the balance statistic approach are employed to estimate a Taylor-type rule. This procedure allows to directly capture a policymaker's assessments of macroeconomic variables that are relevant for its decision making process. As an application of the proposed method the monetary policy of the Bundesbank is re-investigated with a new dataset. One distinctive feature of the Bundesbank's strategy consisted of targeting growth in monetary aggregates. The analysis using the proposed method provides evidence that the Bundesbank indeed took into consideration monetary aggregates but also real economic activity and inflation developments in its monetary policy strategy since 1975

Effectiveness of a clinical practice guideline implementation strategy for patients with anxiety disorders in primary care: cluster randomized trial

<p>Abstract</p> <p>Background</p> <p>Anxiety is a common mental health problem seen in primary care. However, its management in clinical practice varies greatly. Clinical practice guidelines (CPGs) have the potential to reduce variations and improve the care received by patients by promoting interventions of proven benefit. However, uptake and adherence to their recommendations can be low.</p> <p>Method/design</p> <p>This study involves a community based on cluster randomized trial in primary healthcare centres in the Madrid Region (Spain). The project aims to determine whether the use of implementation strategy (including training session, information, opinion leader, reminders, audit, and feed-back) of CPG for patients with anxiety disorders in primary care is more effective than usual diffusion.</p> <p>The number of patients required is 296 (148 in each arm), all older than 18 years and diagnosed with generalized anxiety disorder, panic disorder, and panic attacks by the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV). They are chosen by consecutive sampling.</p> <p>The main outcome variable is the change in two or more points into Goldberg anxiety scale at six and twelve months. Secondary outcome variables include quality of life (EuroQol 5D), and degree of compliance with the CPG recommendations on treatment, information, and referrals to mental health services. Main effectiveness will be analyzed by comparing the patients percentage improvement on the Goldberg scale between the intervention group and the control group. Logistic regression with random effects will be used to adjust for prognostic factors. Confounding factors or factors that might alter the effect recorded will be taken into account in this analysis.</p> <p>Discussion</p> <p>There is a need to identify effective implementation strategies for CPG for the management of anxiety disorders present in primary care. Ensuring the appropriate uptake of guideline recommendations can reduce clinical variation and improve the care patients receive.</p> <p>Trial registration</p> <p>ISRCTN: <a href="http://www.controlled-trials.com/ISRCTN83365316">ISRCTN83365316</a></p

A Socio-Technical and Co-Evolutionary Framework for Reducing Human-Related Risks in Cyber Security and Cybercrime Ecosystems

The focus on cyber security as an interaction between technical elements and humans has typically confined consideration of the latter to practical issues of implementation, conventionally those of `human performance factors' of vigilance etc., 'raising awareness' and/or 'incentivization' of people and organizations to participate and adapt their behavior. But this is far too narrow a view that seriously constrains the ability of cyber security as a whole to adapt and evolve to keep up with adaptive, innovative attackers in a rapidly-changing technological, business and social landscape, in which personal preferences of users are also dynamically evolving. While there is isolated research across different research areas, we noticed the lack of a \emph{holistic} framework combining a range of applicable theoretical concepts (e.g., cultural co-evolution such as technological arms races, opportunity management, behavioral and business models) and technological solutions on reducing human-related risks in the cyber security and cybercrime ecosystems, which involve multiple groups of human actors including offenders, victims, preventers and promoters. This paper reports our ongoing work in developing such a socio-technical framework 1) to allow a more comprehensive understanding of human-related risks within cyber security and cybercrime ecosystems and 2) to support the design of more effective approaches to engaging individuals and organizations in the reduction of such risks. We are in the process of instantiating this framework to encourage behavioral changes in two use cases that capture diverse and complicated socio-technical interactions in cyber-physical systems

Monetary Policy Committee Transparency: Measurement, Determinants, and Economic Effects

This paper studies monetary policy committee transparency (MPCT) based on a new index that measures central bankers' educational and professional backgrounds as disclosed through central bank websites. Based on a novel cross-sectional data set covering 75 central banks, we investigate the determinants of MPCT as well as its economic consequences. We find that past inflation, quality of institutional setup, and extent of Internet use in a country are important determinants of MPCT. MPCT has a robust and significantly negative impact on inflation variability, even after controlling for important macroeconomic variables and institutional transparency, as well as instrumenting MPCT in various ways