Data-driven parameterization of the generalized Langevin equation

We present a data-driven approach to determine the memory kernel and random noise in generalized Langevin equations. To facilitate practical implementations, we parameterize the kernel function in the Laplace domain by a rational function, with coefficients directly linked to the equilibrium statistics of the coarse-grain variables. We show that such an approximation can be constructed to arbitrarily high order and the resulting generalized Langevin dynamics can be embedded in an extended stochastic model without explicit memory. We demonstrate how to introduce the stochastic noise so that the second fluctuation-dissipation theorem is exactly satisfied. Results from several numerical tests are presented to demonstrate the effectiveness of the proposed method

A user-oriented network forensic analyser: the design of a high-level protocol analyser

Network forensics is becoming an increasingly important tool in the investigation of cyber and computer-assisted crimes. Unfortunately, whilst much effort has been undertaken in developing computer forensic file system analysers (e.g. Encase and FTK), such focus has not been given to Network Forensic Analysis Tools (NFATs). The single biggest barrier to effective NFATs is the handling of large volumes of low-level traffic and being able to exact and interpret forensic artefacts and their context – for example, being able extract and render application-level objects (such as emails, web pages and documents) from the low-level TCP/IP traffic but also understand how these applications/artefacts are being used. Whilst some studies and tools are beginning to achieve object extraction, results to date are limited to basic objects. No research has focused upon analysing network traffic to understand the nature of its use – not simply looking at the fact a person requested a webpage, but how long they spend on the application and what interactions did they have with whilst using the service (e.g. posting an image, or engaging in an instant message chat). This additional layer of information can provide an investigator with a far more rich and complete understanding of a suspect’s activities. To this end, this paper presents an investigation into the ability to derive high-level application usage characteristics from low-level network traffic meta-data. The paper presents a three application scenarios – web surfing, communications and social networking and demonstrates it is possible to derive the user interactions (e.g. page loading, chatting and file sharing ) within these systems. The paper continues to present a framework that builds upon this capability to provide a robust, flexible and user-friendly NFAT that provides access to a greater range of forensic information in a far easier format

Heard melodies are sweet, but those unheard are sweeter : understanding corruption using cross-national firm-level surveys

Since the early 1990s, a large number of studies have been undertaken to understand the causes and consequences of corruption. Many of these studies have employed firm-level survey data from various countries. While insightful, these analyses based on firm-level surveys have largely ignored two important potential problems: nonresponse and false response by the firms. Treating firms'responses on a sensitive issue like corruption at their face value could produce incorrect inferences and erroneous policy recommendations. We argue that the data generation of nonresponse and false response is a function of the political environment in which the firms operate. In a politically repressive environment, firms use nonresponse and false response as self-protection mechanisms. Corruption is understated as a result. We test our arguments using the World Bank enterprise survey data of more than 44,000 firms in 72 countries for the period 2000-2005 and find that firms in countries with less press freedom are more likely to provide nonresponse or false response on the issue of corruption. Therefore, ignoring this systematic bias in firms'responses could result in underestimation of the severity of corruption in politically repressive countries. More important, this bias is a rich and underutilized source of information on the political constraints faced by the firms. Nonresponse and false response, like unheard melodies, could be more informative than the heard melodies in the available truthful responses in firm surveys.Public Sector Corruption&Anticorruption Measures,Microfinance,Access to Finance,Poverty Monitoring&Analysis,Social Accountability