A dubiety-determining based model for database cumulated anomaly intrusion

The concept of Cumulated Anomaly (CA), which describes a new type of database anomalies, is addressed. A typical CA intrusion is that when a user who is authorized to modify data records under certain constraints deliberately hides his/her intentions to change data beyond constraints in different operations and different transactions. It happens when some appearing to be authorized and normal transactions lead to certain accumulated results out of given thresholds. The existing intrusion techniques are unable to deal with CAs. This paper proposes a detection model, Dubiety-Determining Model (DDM), for Cumulated Anomaly. This model is mainly based on statistical theories and fuzzy set theories. It measures the dubiety degree, which is presented by a real number between 0 and 1, for each database transaction, to show the likelihood of a transaction to be intrusive. The algorithms used in the DDM are introduced. A DDM-based software architecture has been designed and implemented for monitoring database transactions. The experimental results show that the DDM method is feasible and effective

A New Automatic Method to Identify Galaxy Mergers I. Description and Application to the STAGES Survey

We present an automatic method to identify galaxy mergers using the morphological information contained in the residual images of galaxies after the subtraction of a Sersic model. The removal of the bulk signal from the host galaxy light is done with the aim of detecting the fainter minor mergers. The specific morphological parameters that are used in the merger diagnostic suggested here are the Residual Flux Fraction and the asymmetry of the residuals. The new diagnostic has been calibrated and optimized so that the resulting merger sample is very complete. However, the contamination by non-mergers is also high. If the same optimization method is adopted for combinations of other structural parameters such as the CAS system, the merger indicator we introduce yields merger samples of equal or higher statistical quality than the samples obtained through the use of other structural parameters. We explore the ability of the method presented here to select minor mergers by identifying a sample of visually classified mergers that would not have been picked up by the use of the CAS system, when using its usual limits. Given the low prevalence of mergers among the general population of galaxies and the optimization used here, we find that the merger diagnostic introduced in this work is best used as a negative merger test, i.e., it is very effective at selecting non-merging galaxies. As with all the currently available automatic methods, the sample of merger candidates selected is contaminated by non-mergers, and further steps are needed to produce a clean sample. This merger diagnostic has been developed using the HST/ACS F606W images of the A901/02 cluster (z=0.165) obtained by the STAGES team. In particular, we have focused on a mass and magnitude limited sample (log M/M_{O}>9.0, R_{Vega}<23.5mag)) which includes 905 cluster galaxies and 655 field galaxies of all morphological types.Comment: 25 pages, 14 figures, 4 tables. To appear in MNRA

Asymmetric Social Interaction in Economics: Cigarette Smoking Among Young People in the United States, 1992-1999

We analyzed cigarette smoking among people aged 15 - 24 in approximately 90,000 households in the 1992 - 1999 U.S. Current Population Surveys. We modeled social influence as an informational externality, in which each young person's smoking informs her peers about its coolness.' The resulting family smoking game,' with each sibling's smoking endogenous, may have multiple equilibria. We found that the pro-smoking influence of a fellow smoker markedly exceeded the deterrent effect of a non-smoking peer. The phenomenon of asymmetric social influence has implications for financial markets, educational performance, criminal behavior, and other areas of inquiry where peer influence is important.

Biological Carbon Sequestration and Carbon Trading Re-Visited

Biological activities that sequester carbon create CO2 offset credits that could obviate the need for reductions in fossil fuel use. Credits are earned by storing carbon in terrestrial ecosystems and wood products, although CO2 emissions are also mitigated by delaying deforestation, which accounts for one-quarter of anthropogenic CO2 emissions. However, non-permanent carbon offsets from biological activities are difficult to compare with each other and with emissions reduction because they differ in how long they prevent CO2 from entering the atmosphere. This is the duration problem. It results in uncertainty and makes it hard to determine the legitimacy of biological activities in mitigating climate change. Measuring, verifying and monitoring the carbon sequestered in sinks greatly increases transaction costs and leads to rent seeking by sellers of dubious sink credits. While biological sink activities undoubtedly help mitigate climate change and should not be neglected, it is shown that there are limits to the substitutability between temporary offset credits from these activities and emissions reduction, and that this has implications for carbon trading. A possible solution to inherent incommensurability between temporary and permanent credits is also suggested

A Neural Network Method for Efficient Vegetation Mapping

This paper describes the application of a neural network method designed to improve the efficiency of map production from remote sensing data. Specifically, the ARTMAP neural network produces vegetation maps of the Sierra National Forest, in Northern California, using Landsat Thematic Mapper (TM) data. In addition to spectral values, the data set includes terrain and location information for each pixel. The maps produced by ARTMAP are of comparable accuracy to maps produced by a currently used method, which requires expert knowledge of the area as well as extensive manual editing. In fact, once field observations of vegetation classes had been collected for selected sites, ARTMAP took only a few hours to accomplish a mapping task that had previously taken many months. The ARTMAP network features fast on-line learning, so the system can be updated incrementally when new field observations arrive, without the need for retraining on the entire data set. In addition to maps that identify lifeform and Calveg species, ARTMAP produces confidence maps, which indicate where errors are most likely to occur and which can, therefore, be used to guide map editing

Finding Streams in Knowledge Graphs to Support Fact Checking

The volume and velocity of information that gets generated online limits current journalistic practices to fact-check claims at the same rate. Computational approaches for fact checking may be the key to help mitigate the risks of massive misinformation spread. Such approaches can be designed to not only be scalable and effective at assessing veracity of dubious claims, but also to boost a human fact checker's productivity by surfacing relevant facts and patterns to aid their analysis. To this end, we present a novel, unsupervised network-flow based approach to determine the truthfulness of a statement of fact expressed in the form of a (subject, predicate, object) triple. We view a knowledge graph of background information about real-world entities as a flow network, and knowledge as a fluid, abstract commodity. We show that computational fact checking of such a triple then amounts to finding a "knowledge stream" that emanates from the subject node and flows toward the object node through paths connecting them. Evaluation on a range of real-world and hand-crafted datasets of facts related to entertainment, business, sports, geography and more reveals that this network-flow model can be very effective in discerning true statements from false ones, outperforming existing algorithms on many test cases. Moreover, the model is expressive in its ability to automatically discover several useful path patterns and surface relevant facts that may help a human fact checker corroborate or refute a claim.Comment: Extended version of the paper in proceedings of ICDM 201

Teaching old sensors New tricks: archetypes of intelligence

In this paper a generic intelligent sensor software architecture is described which builds upon the basic requirements of related industry standards (IEEE 1451 and SEVA BS- 7986). It incorporates specific functionalities such as real-time fault detection, drift compensation, adaptation to environmental changes and autonomous reconfiguration. The modular based structure of the intelligent sensor architecture provides enhanced flexibility in regard to the choice of specific algorithmic realizations. In this context, the particular aspects of fault detection and drift estimation are discussed. A mixed indicative/corrective fault detection approach is proposed while it is demonstrated that reversible/irreversible state dependent drift can be estimated using generic algorithms such as the EKF or on-line density estimators. Finally, a parsimonious density estimator is presented and validated through simulated and real data for use in an operating regime dependent fault detection framework