Express: a web-based technology to support human and computational experimentation

Experimental cognitive psychology has been greatly assisted by the development of general computer-based experiment presentation packages. Typically, however, such packages provide little support for running participants on different computers. It is left to the experimenter to ensure that group sizes are balanced between conditions and to merge data gathered on different computers once the experiment is complete. Equivalent issues arise in the evaluation of parameterized computational models, where it is frequently necessary to test a model's behavior over a range of parameter values (which amount to between-subjects factors) and where such testing can be speeded up significantly by the use of multiple processors. This article describes Express, a Web-based technology for coordinating "clients" (human participants or computational models) and collating client data. The technology provides an experiment design editor, client coordination facilities (e.g., automated randomized assignment of clients to groups so that group sizes are balanced), general data collation and tabulation facilities, a range of basic statistical functions (which are constrained by the specified experimental design), and facilities to export data to standard statistical packages (such as SPSS). We report case studies demonstrating the utility of Express in both human and computational experiments. Express may be freely downloaded from the Express Web site (http://express.psyc.bbk.ac.uk/)

Diagnose network failures via data-plane analysis

Diagnosing problems in networks is a time-consuming and error-prone process. Previous tools to assist operators primarily focus on analyzing control plane configuration. Configuration analysis is limited in that it cannot find bugs in router software, and is harder to generalize across protocols since it must model complex configuration languages and dynamic protocol behavior. This paper studies an alternate approach: diagnosing problems through static analysis of the data plane. This approach can catch bugs that are invisible at the level of configuration files, and simplifies unified analysis of a network across many protocols and implementations. We present Anteater, a tool for checking invariants in the data plane. Anteater translates high-level network invariants into boolean satisfiability problems, checks them against network state using a SAT solver, and reports counterexamples if violations have been found. Applied to a large campus network, Anteater revealed 23 bugs, including forwarding loops and stale ACL rules, with only five false positives. Nine of these faults are being fixed by campus network operators

An extensive English language bibliography on graph theory and its applications, supplement 1

Graph theory and its applications - bibliography, supplement

Using Localised ‘Gossip’ to Structure Distributed Learning

The idea of a “memetic” spread of solutions through a human culture in parallel to their development is applied as a distributed approach to learning. Local parts of a problem are associated with a set of overlappingt localities in a space and solutions are then evolved in those localites. Good solutions are not only crossed with others to search for better solutions but also they propogate across the areas of the problem space where they are relatively successful. Thus the whole population co-evolves solutions with the domains in which they are found to work. This approach is compared to the equivalent global evolutionary computation approach with respect to predicting the occcurence of heart disease in the Cleveland data set. It greatly outperforms the global approach, but the space of attributes within which this evolutionary process occurs can effect its efficiency