Suchergebnisse

Although studies show that actions by property owners, such as maintaining a defensible space, are generally the best means of protecting property from wildfire, victims often blame government agencies and others for property damage, injury, and death. This article describes a multiple-methods approach for investigating factors that influence how people who experience wildfire perceive the cause of wildfire damage. Phase I and II mail surveys and real-time field interviews were conducted in communities on the western slope of the Sierra Nevada. Generally speaking, people who had experienced wildfire attributed damage to other people's actions more than people who had not. Whether residents incurred damage or not, having maintained a sense of control or interacting with firefighters also appears to have influenced attributions. We argue that multiple-methods approaches to such questions have the potential to reveal more about such phenomena than approaches based on any single method.

We describe a simple yet rigorous graphical method for eliminating bias in theory-based program evaluation. The method is an application to social and international development program evaluation of the graphical causal models used to test medical treatments. We implement a graphical causal model for the World Bank's well-known Bangladesh Integrated Nutrition Project. We show how to construct the graphical causal model to represent program theory in context in explicitly causal terms. We then show how to visually inspect the graphical causal model to distinguish causal from non-causal associations between variables in evaluation data. Finally, we show how to select a set of adjustment variables to neutralize non-causal associations, eliminating bias in all forms of causal inference—qualitative and quantitative, linear and non-linear.

In February 2010, former NSA Director Mike McConnell wrote that, "We need to develop an early- warning system to monitor cyberspace, identify intrusions and locate the source of attacks with a trail of evidence that can support diplomatic, military and legal options—and we must be able to do this in milliseconds. More specifically, we need to reengineer the Internet to make attribution, geolocation, intelligence analysis and impact assessment—who did it, from where, why and what was the result—more manageable."2 This statement is part of a recurring theme that a secure Internet must provide better attribution for actions occurring on the network. Although attribution generally means assigning a cause to an action, this meaning refers to identifying the agent responsible for the action (specifically, "determining the identity or location of an attacker or an attacker's intermediary"3). This links the word to the more general idea of identity, in its various meanings. Attribution is central to deterrence, the idea that one can dissuade attackers from acting through fear of some sort of retaliation. Retaliation requires knowing with full certainty who the attackers are. The Internet was not designed with the goal of deterrence in mind, and perhaps a future Internet should be designed differently. In particular, there have been calls for a stronger form of personal identification that can be observed in the network. A non-technical version of this view was put forward as: "Why don't packets have license plates?" This is called the attribution problem. There are many types of attribution, and different types are useful in different contexts. We believe that what has been described as the attribution problem is actually a number of problems rolled together. Attribution is certainly not one size fits all. ; This material is based on work supported by the U.S. Office of Naval Research, Grant No. N00014-09-1-0597. Any opinions, findings, conclusions or recommendations therein are those of the author(s) and do not necessarily reflect the views of the Office of Naval Research.

The statistics course most engineers took in college—seldom more than one—introduced them to a particular species of statistics, which, regrettably, is of limited use in geotechnical practice: frequentist sampling theory. That species of statistical thinking arose to address problems of agricultural experimentation, biology, and economics. It is mostly applicable to narrow domains in medical trials, political polls, and the like, where carefully planned experiments lead to large databases and p-value tests of hypotheses. These are not the problems facing the geotechnical engineer. He or she faces extremely limited numbers of observations (maybe only one), measurements of differing types and quality, a blend of qualitative and quantitative information, and a need to make sequential decisions as data arrive.

In our paper we present a corpus of transcribed Lithuanian parliamentary speeches. The corpus is prepared in a specific format, appropriate for different authorship identification tasks. The corpus consists of approximately 111 thousand texts (24 million words). Each text matches one parliamentary speech produced during an ordinary session from the period of 7 parliamentary terms starting on March 10, 1990 and ending on December 23, 2013. The texts are grouped into 147 categories corresponding to individual authors, therefore they can be used for authorship attribution tasks; besides, these texts are also grouped according to age, gender and political views, therefore they are also suitable for author profiling tasks. Whereas short texts complicate recognition of author speaking style and are ambiguous in relation to the style of other authors, we incorporated only texts containing not less than 100 words into the corpus. In order to make each category as comprehensive and representative as possible, we included only those authors, who produced speeches at least 200 times. All the texts are lemmatized, morphologically and syntactically annotated, tokenized into the character n-grams. The statistical information of the corpus is also available. We have also demonstrated that the created corpus can be effectively used in authorship attribution and author profiling tasks with supervised machine learning methods. The corpus structure also allows using it with unsupervised machine learning methods and can be used for creation of rule-based methods, as well as in different linguistic analyses.

In our paper we present a corpus of transcribed Lithuanian parliamentary speeches. The corpus is prepared in a specific format, appropriate for different authorship identification tasks. The corpus consists of approximately 111 thousand texts (24 million words). Each text matches one parliamentary speech produced during an ordinary session from the period of 7 parliamentary terms starting on March 10, 1990 and ending on December 23, 2013. The texts are grouped into 147 categories corresponding to individual authors, therefore they can be used for authorship attribution tasks; besides, these texts are also grouped according to age, gender and political views, therefore they are also suitable for author profiling tasks. Whereas short texts complicate recognition of author speaking style and are ambiguous in relation to the style of other authors, we incorporated only texts containing not less than 100 words into the corpus. In order to make each category as comprehensive and representative as possible, we included only those authors, who produced speeches at least 200 times. All the texts are lemmatized, morphologically and syntactically annotated, tokenized into the character n-grams. The statistical information of the corpus is also available. We have also demonstrated that the created corpus can be effectively used in authorship attribution and author profiling tasks with supervised machine learning methods. The corpus structure also allows using it with unsupervised machine learning methods and can be used for creation of rule-based methods, as well as in different linguistic analyses.