Abstract
One emergent, widely used metaphor and rich source of inspiration for computer security has been the vertebrate Immune System (IS). This is mainly due to its intrinsic nature of having to constantly protect the body against harm inflicted by external (non-self) harmful entities. The bridge between metaphor and the reality of new practical systems for anomaly detection is cemented by recent biological advancements and new proposed theories on the dynamics of immune cells by the field of theoretical immunology. In this paper we present a work in progress research on the deployment of an immune-inspired architecture, based on Grossman's Tunable Activation Threshold (TAT) hypothesis, for temporal anomaly detection, where there is a strict temporal ordering on the data, such as network intrusion detection. We start by briefly describing the overall architecture. Then, we present some preliminary results obtained in a Production network. Finally, we conclude by presenting the main lines of research we intend to pursue in the near future.

Abstract
Current Learning Management Systems generically provide online forums for interactions between students and educators. In this article we propose a tool, the iGraph, that can be embedded in Learning Management Systems that feature hierarchical forums. The iGraph is capable of depicting and analyzing online interactions in an easy to understand graph. The positioning algorithm is based on social network analysis statistics, taken from the collected interactions, and is able to smoothly present temporal evolution in order to find communicational patterns and report them to the educator.

Abstract
Climate change is expected to involve not only changes in the mean of climate parameters, but also in the characteristics of the corresponding seasonal cycle. However, the discrimination from an observational record of long-term changes in the mean and low-frequency variations in the seasonal pattern is a challenging task, requiring the application of specific statistical methods. In this work, a time series decomposition method based on autoregression is applied in order to obtain a flexible description of seasonal variability from European temperature records. The method is based on the dynamic linear model representation for an autoregressive process and is particularly useful for isolating time-varying cycles in climate time series, allowing to retrieve fluctuations in the amplitude and phase of the periodic components and to assess their statistical significance. This approach is utilised in the analysis of long time series of daily mean temperature from the ECA (European Climate Assessment) project. Seasonality in Europe's air temperature is characterised by an annual cycle with a stable phase but considerable inter-annual and inter-decadal variability. In particular, the annual amplitude was highest in the 1940's and exhibits a distinct minimum around 1975, coincident with the climatic regime shift of the mid-1970's.

Abstract
This paper introduces a generic technique to obtain a shared-storage database cluster from an off-the-shelf database management system, without needing to heavily refactor server software to deal with distributed locking, buffer invalidation, and recovery from partial cluster failure. Instead, the core of the proposal is the combination of a replication protocol and a surprisingly simple modification to the common copy-on-write logical volume management technique: One of the servers is allowed to skip copy-on-write and directly update the original backing store. This makes it possible to use any shared-nothing database server software in a shared or partially shared storage configuration, thus allowing large cluster configurations with a small number of copies of data. Copyright 2009 ACM.

Abstract
The detection of anomalies in computer environments, like network intrusion detection, computer virus or spam classification, is usually based on some form of pattern search on a database of "signatures " for known anomalies. Although very successful and widely deployed, these approaches are only able to cope with anomalous events that have already been seen. To cope with these weaknesses, the "behaviour" based systems has been deployed. Although conceptually more appealing, they have still an impractical high rate of false alarms. The vertebrate Immune System is an emergent and appealing metaphor for new ideas on anomaly detection, being already adopted some algorithms and theoretical theories in particular fields, such as network intrusion detection. In this paper we present a temporal anomaly detection architecture based on the Grossman's Tunable Activation Threshold (TAT) hypothesis. The basic idea is that the repertoire of immune cells is constantly tuned according to the cells temporal interactions with the environment and yet retains responsiveness to an open-ended set of abnormal events. We describe some preliminary work on the development of an anomaly detection algorithm derived from TAT and present the results obtained thus far using some synthetic data-sets.

Abstract
Isolating long-term trend in sea surface temperature (SST) from El Nino southern oscillation (ENSO) variability is fundamental for climate studies. In the present study, trend-empirical orthogonal function (EOF) analysis, a robust space-time method for extracting trend patterns, is applied to isolate low-frequency variability from time series of SST anomalies for the 1982-2006 period. The first derived trend pattern reflects a systematic decrease in SST during the 25-year period in the equatorial Pacific and an increase in most of the global ocean. The second trend pattern reflects mainly ENSO variability in the Pacific Ocean. The examination of the contribution of these low-frequency modes to the globally averaged SST fluctuations indicates that they are able to account for most (>90%) of the variability observed in global mean SST. Trend-EOFs perform better than conventional EOFs when the interest is on low-frequency rather than on maximum variance patterns, particularly for short time series such as the ones resulting from satellite retrievals. Copyright (C) 2009 Royal Meteorological Society