Quantifying the association between discrete event time series with applications to digital forensics

We consider the problem of quantifying the degree of association between pairs of discrete event time series, with potential applications in forensic and cybersecurity settings. We focus in particular on the case where two associated event series exhibit temporal clustering such that the occurrence of one type of event at a particular time increases the likelihood that an event of the other type will also occur nearby in time. We pursue a non‐parametric approach to the problem and investigate various score functions to quantify association, including characteristics of marked point processes and summary statistics of interevent times. Two techniques are proposed for assessing the significance of the measured degree of association: a population‐based approach to calculating score‐based likelihood ratios when a sample from a relevant population is available, and a resampling approach to computing coincidental match probabilities when only a single pair of event series is available. The methods are applied to simulated data and to two real world data sets consisting of logs of computer activity and achieve accurate results across all data sets

Perceived strength of forensic scientists’ reporting statements about source conclusions

Three studies investigated lay people’s perceptions of the relative strength of various conclusions that a forensic scientist might present about whether two items (fingerprints, biological samples) have a common source. Lay participants made a series of judgments about which of two conclusions seemed stronger for proving the items had a common source. The data were fitted to Thurstone–Mosteller paired comparison models to obtain rank-ordered lists of the various statements and an indication of the perceived differences among them. The results reveal the perceived strength of several types of statements, relative to one another, including verbal statements regarding strength of support (e.g. ‘extremely strong support for same source’), source probability statements (e.g. ‘highly probable same source’), random match probabilities (e.g. RMP = 1 in 100 000), likelihood ratios, and categorical statements (e.g. ‘identification’). These comparisons in turn provide insight into whether particular statements about the strength of forensic evidence convey the intended meaning and will be interpreted in a manner that is justifiable and appropriate

Statistical Methods for the Forensic Analysis of Geolocated Event Data

A common question in forensic analysis is whether two observed data sets originated from the same source or from different sources. Statistical approaches to addressing this question have been widely adopted within the forensics community, particularly for DNA evidence. Here we investigate the application of statistical approaches to same-source forensic questions for spatial event data, such as determining the likelihood that two sets of observed GPS locations were generated by the same individual. We develop two approaches to quantify the strength of evidence in this setting. The first is a likelihood ratio approach based on modeling the spatial event data directly. The second approach is to instead measure the similarity of the two observed data sets via a score function and then assess the strength of the observed score resulting in the score-based likelihood ratio. A comparative evaluation using geolocated Twitter event data from two large metropolitan areas shows the potential efficacy of such techniques

Biometric Performance as a Function of Gallery Size

Many developers of biometric systems start with modest samples before general deployment. They are interested in how their systems will work with much larger samples. We evaluated the effect of gallery size on biometric performance. Identification rates describe the performance of biometric identification, whereas ROC-based measures describe the performance of biometric authentication (verification). Therefore, we examined how increases in gallery size affected identification rates (i.e., Rank-1 Identification Rate, or Rank-1 IR) and ROC-based measures such as equal error rate (EER). We studied these phenomena with synthetic data as well as real data from a face recognition study. It is well known that the Rank-1 IR declines with increasing gallery size. We have provided further insight into this decline. We have shown that this relationship is linear in log(Gallery Size). We have also shown that this decline can be counteracted with the inclusion of additional information (features) for larger gallery sizes. We have also described the curves which can be used to predict how much additional information is required to stabilize the Rank-1 IR as a function of gallery size. These equations are also linear in log(gallery size). We have also shown that the entire ROC curve is not systematically affected by gallery size, and so ROC-based scalar performance metrics such as EER are also stable across gallery size.Comment: 19 pages, 9 Figures, 0 Table

Experiences of COVID-19-Related Racism and Impact on Depression Trajectories Among Racially/Ethnically Minoritized Adolescents

Purpose In 2020, racially/ethnically minoritized (REMD) youth faced the “dual pandemics” of COVID-19 and racism, both significant stressors with potential for adverse mental health effects. The current study tested whether short- and long-term trajectories of depressive symptoms from before to during the COVID-19 pandemic differed between REMD adolescents who did and did not endorse exposure to COVID-19-era-related racism (i.e., racism stemming from conditions created or exacerbated by the COVID-19 pandemic). Methods A community sample of 100 REMD adolescents enrolled in an ongoing longitudinal study of mental health was assessed before and during the COVID-19 pandemic. Participants were 51% girls, mean age = 16, standard deviation = 2.7, and identified as Latinx/Hispanic (48%), Multiethnic (34%), Asian American (12%), and Black (6%). Results REMD adolescents\u27 depressive symptoms were elevated during the COVID-19 pandemic compared to pre-pandemic levels, and increases were more pronounced over time for those who endorsed exposure to COVID-19-era-related racism. In general, Asian American participants endorsed racism experiences at the highest rates compared to others, including being called names (42%), people acting suspicious around them (33%), and being verbally threatened (17%). Additionally, more than half of Black and Asian American participants reported worry about experiencing racism related to the COVID-19 pandemic, even if they had not experienced it to date. Discussion REMD adolescents are at increased risk for depressive symptoms related to converging stressors stemming from the COVID-19 pandemic and pandemic-related racism, which has the potential to widen racial/ethnic mental health disparities faced by the REMD youth