The ubiquity of 802.11 devices and networks enables anyone to track our every move with alarming ease. Each 802.11 device transmits a globally unique and persistent MAC address and thus is trivially identifiable. In response, recent research has proposed replacing such identifiers with pseudonyms (i.e., temporary, unlinkable names). In this paper, we demonstrate that pseudonyms are insufficient to prevent tracking of 802.11 devices because implicit identifiers, or identifying characteristics of 802.11 traffic, can identify many users with high accuracy. For example, even without unique names and addresses, we estimate that an adversary can identify 64 % of users with 90 % accuracy when they spend a day at a busy hot spot. We present an automated procedure based on four previously unrecognized implicit identifiers that can identify users in three real 802.11 traces even when pseudonyms and encryption are employed. We find that the majority of users can be identified using our techniques, but our ability to identify users is not uniform; some users are not easily identifiable. Nonetheless, we show that even a single implicit identifier is sufficient to distinguish many users. Therefore, we argue that design considerations beyond eliminating explicit identifiers (i.e., unique names and addresses), must be addressed in order to prevent user tracking in wireless networks. Categories and Subject Descriptors:

There is an active debate within the field of phonology concerning the cognitive status of substantive phonetic factors such as ease of articulation and perceptual distinctiveness. A new framework is proposed in which substance acts as a bias, or prior, on phonological learning. Two experiments tested this framework with a method in which participants are first provided highly impoverished evidence of a new phonological pattern, and then tested on how they extend this pattern to novel contexts and novel sounds. Participants were found to generalize velar palatalization (e.g., the change from [k]asinkeep to [t�ʃ]asincheap) in a way that accords with linguistic typology, and that is predicted by a cognitive bias in favor of changes that relate perceptually similar sounds. Velar palatalization was extended from the mid front vowel context (i.e., before [e]asincape) to the high front vowel context (i.e., before [i]asin keep), but not vice versa. The key explanatory notion of perceptual similarity is quantified with a psychological model of categorization, and the substantively biased framework is formalized as a conditional random field. Implications of these results for the debate on substance, theories of phonological generalization, and the formalization of similarity are discussed.

In some contexts it may be useful to predict the latency for short TCP transfers. For example, a Web server could automatically tailor its content depending on the network path to each client, or an "opportunistic networking" application could improve its scheduling of data transfers.

Methods of sample size planning are developed from the accuracy in parameter approach in the multiple regression context in order to obtain a sufficiently narrow confidence interval for the population squared multiple correlation coefficient when regressors are random. Approximate and exact methods are developed that provide necessary sample size so that the expected width of the confidence interval will be sufficiently narrow. Modifications of these methods are then developed so that necessary sample size will lead to sufficiently narrow confidence intervals with no less than some desired degree of assurance. Computer routines have been developed and are included within the MBESS R package so that the methods discussed in the article can be implemented. The methods and computer routines are demonstrated using an empirical example linking innovation in the health services industry with previous innovation, personality factors, and group climate characteristics.

We present a visual exploration of the field of human-computer interaction through the author and article metadata of four of its major conferences: the ACM conferences on Computer-Human Interaction (CHI), User Interface Software and Technology (UIST) and

NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Memory and Language. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A new framework is offered that uses multilevel logistic regression (MLR) to analyze data from ‘visual world ’ eyetracking experiments used in psycholinguistic research. The MLR framework overcomes some of the problems using conventional analyses, making it possible to incorporate time as a continuous variable and gaze location as a categorical dependent variable. The multilevel approach minimizes the need for data aggregation and thus provides a more statistically powerful approach. With MLR, the researcher builds a mathematical model of the overall response curve that separates the response into different temporal components. The researcher can test hypotheses by examining the impact of independent variables and their interactions on these components. A worked example using MLR is provided. The current article provides solutions for the analysis of data sets from eyetracking experiments that use the ‘visual world’

Human-computer systems intended for time-critical multitasking need to be designed with an understanding of how humans can coordinate and interleave perceptual, memory, and motor processes. This paper presents human performance data for a highly-practiced time-critical dual task. In the first of the two interleaved tasks, participants tracked a target with a joystick. In the second, participants keyed-in responses to objects moving across a radar display. Task manipulations include the peripheral visibility of the secondary display (visible or not) and the presence or absence of auditory cues to assist with the radar task. Eye movement analyses reveal extensive coordination and overlapping of human information processes and the extent to which task manipulations helped or hindered dual task performance. For example, auditory cues helped only a little when the secondary display was peripherally visible, but they helped a lot when it was not peripherally visible. Author Keywords Auditory displays, cognitive strategies, eye tracking, multimodal, multitasking, visual displays. ACM Classification Keywords

In multiple testing, hypotheses can often be assessed by multivariate test statistics. We study how to use such statistics to control false discovery rate (FDR) with reasonable power and capacity to control the so-called positive FDR (pFDR). We show that this can be done by using nested regions of multivariate p-values derived from the test statistics. If the distributions of the test statistics are known, then, by choosing the regions appropriately, the FDR can be controlled while the power is maximized. Our focus, however, is how to select nested regions when the distributions of the test statistics are only partially known. Under certain conditions, the procedure based on our proposed nested regions has asymptotically maximum power as its pFDR control level approaches the attainable limit. The dependency of the procedure’s performance on its parameters is studied. The procedure is also compared with those based on nested regions that are easier to construct as well as those based on more straightforward combinations of the test statistics. Both analysis and simulation give favorable results for the former.

We consider power calculation in structural equation modeling with data missing completely at random (MCAR). Muthén and Muthén (2002) recently demonstrated how power calculations with data MCAR can be carried out by means of a Monte Carlo study. Here we show that the method of Satorra and Saris (1985), which is based on the nonnull distribution of the (normal theory) log-likelihood ratio test, can also be used. Compared to a Monte Carlo study, this method is computationally less intensive. We discuss 2 ways to calculate power when data are MCAR, one based on multigroup analysis and summary statistics, the other based on transformed raw data. The latter method is quite simple to carry out. Four examples are presented. This article is limited to data MCAR. Generally MCAR is a strong assumption. We demonstrate that results of power analyses based on the MCAR assumption are not informative if the data are actually missing at random. In structural equation modeling (SEM), power calculation based on the normal theory likelihood ratio test (LRT) has been developed and discussed by Satorra and Saris (1985; Saris & Satorra, 1993). In this method, power is calculated by integrating the nonnull distribution of the LRT. This method is quite easy to carry out, but is based on the various assumptions associated with the normal theory LRT, such as large samples and multivariate normality (Azzelini, 1996; Bollen, 1989) and small to moderate misspecification (Curran, Bollen, Paxton, Kirby, & Chen, 2002). An alternative approach to power calculation is based on the empirical, rather than the theoretical distribution of the test statistic (Lei & Dunbar, 2004;

In this paper we will present a corpus-based cognitive-semantic analysis of five verbs that express 'begin ' in English and Russian, i.e. begin, start, načat', načat'sja and stat'. On the basis of a quantitative analysis of data extracted from the ICE-GB and the Uppsala Corpus we conclude that the prototype for each verb and set of verbs in each language revolves around a different set of characteristics altogether: the difference between begin and start is lexical in nature, that between načat ' and stat ' can be described as aspectual, whereas the differences between načat ' and načat'sja should be termed argument structural. Dissimilarities like these that are of an entirely different order can only be picked up if a methodology is used that adequately captures the multivariate nature of the phenomenon; the Behavioral Profiling approach we have developed and applied here does exactly that.