Modelling the time to detection of urban tuberculosis in two big cities in Portugal:a spatial survival analysis

SETTING: Portuguese National Tuberculosis Control Programme. OBJECTIVE: To examine delays in tuberculosis (TB) diagnosis using a spatial component in two high-incidence cities, Lisbon and Oporto, in Portugal, a low-incidence country. DESIGN: A retrospective nationwide study was conducted based on official TB data between 2010 and 2013 to analyse diagnostic delays at the lowest administrative level (freguesias) using spatial survival analyses, taking into account individual level covariates. RESULTS: Median diagnostic delays in Lisbon (n = 2706 cases) and Oporto (n = 1883) were respectively 62 (range 1–359, mean 81.01) and 60 days (range 1–3544, mean 79.5). In both cities, case detection rates initially rose until 50 days, then stabilised, but rose again at about 200 days. Diagnostic delay was significantly shorter among males and human immunodeficiency virus positive individuals in both cities, but was significantly longer among migrants in Lisbon. There is evidence of spatial correlation between freguesias; different spatial patterns were observed in diagnostic delays and in likelihood of case detection. CONCLUSION: These results are concordant with existing literature. The two study areas present considerable spatial variations in diagnostic delay, highlighting the fact that large cities should not be treated as homogeneous entities. The potential of spatial survival methods in spatial epidemiology is highlighted

Comparing the utility of user-level and kernel-level data for dynamic malware analysis

Dynamic malware analysis is fast gaining popularity over static analysis since it is not easily defeated by evasion tactics such as obfuscation and polymorphism. During dynamic analysis, it is common practice to capture the system calls that are made to better understand the behaviour of malware. System calls are captured by hooking certain structures in the Operating System. There are several hooking techniques that broadly fall into two categories, those that run at user-level and those that run at kernel level. User-level hooks are currently more popular despite there being no evidence that they are better suited to detecting malware. The focus in much of the literature surrounding dynamic malware analysis is on the data analysis method over the data capturing method. This thesis, on the other hand, seeks to ascertain if the level at which data is captured affects the ability of a detector to identify malware. This is important because if the data captured by the hooking method most commonly used is sub-optimal, the machine learning classifier can only go so far. To study the effects of collecting system calls at different privilege levels and viewpoints, data was collected at a process-specific user-level using a virtualised sandbox environment and a systemwide kernel-level using a custom-built kernel driver for all experiments in this thesis. The experiments conducted in this thesis showed kernel-level data to be marginally better for detecting malware than user-level data. Further analysis revealed that the behaviour of malware used to differentiate it differed based on the data given to the classifiers. When trained on user-level data, classifiers used the evasive features of malware to differentiate it from benignware. These are the very features that malware uses to avoid detection. When trained on kernel-level data, the classifiers preferred to use the general behaviour of malware to differentiate it from benignware. The implications of this were witnessed when the classifiers trained on user-level and kernel-level data were made to classify malware that had been stripped of its evasive properties. Classifiers trained on user-level data could not detect malware that only possessed malicious attributes. While classifiers trained on kernel-level data were unable to detect malware that did not exhibit the amount of general activity they expected in malware. This research highlights the importance of giving careful consideration to the hooking methodology employed to collect data, since it not only affects the classification results, but a classifier’s understanding of malware

Uncertainty Quantification in Breakup Reactions

Breakup reactions are one of the favored probes to study loosely bound nuclei, particularly those in the limit of stability forming a halo. In order to interpret such breakup experiments, the continuum discretized coupled channel method is typically used. In this study, the first Bayesian analysis of a breakup reaction model is performed. We use a combination of statistical methods together with a three-body reaction model (the continuum discretized coupled channel method) to quantify the uncertainties on the breakup observables due to the parameters in the effective potential describing the loosely bound projectile of interest. The combination of tools we develop opens the path for a Bayesian analysis of not only breakup processes, but also a wide array of complex processes that require computationally intensive reaction models

Cybersecurity of Industrial Cyber-Physical Systems: A Review

Industrial cyber-physical systems (ICPSs) manage critical infrastructures by controlling the processes based on the "physics" data gathered by edge sensor networks. Recent innovations in ubiquitous computing and communication technologies have prompted the rapid integration of highly interconnected systems to ICPSs. Hence, the "security by obscurity" principle provided by air-gapping is no longer followed. As the interconnectivity in ICPSs increases, so does the attack surface. Industrial vulnerability assessment reports have shown that a variety of new vulnerabilities have occurred due to this transition while the most common ones are related to weak boundary protection. Although there are existing surveys in this context, very little is mentioned regarding these reports. This paper bridges this gap by defining and reviewing ICPSs from a cybersecurity perspective. In particular, multi-dimensional adaptive attack taxonomy is presented and utilized for evaluating real-life ICPS cyber incidents. We also identify the general shortcomings and highlight the points that cause a gap in existing literature while defining future research directions.Comment: 32 pages, 10 figure

Discussion of "Should we sample more frequently?: decision support via multirate spectrum estimation" by G. P. Nason, B. Powell, D. Elliott and P. A. Smith

Discussion as part of Nason, G. P., Powell, B., Elliott, D. and Smith, P. A. (2017), Should we sample a time series more frequently?: decision support via multirate spectrum estimation. J. R. Stat. Soc. A, 180: 353–407. doi:10.1111/rssa.1221

Six Student Projects from the North Florida Editorial Workshop

In this presentation, six students will discuss digital editing projects they have carried out through the North Florida Editorial Workshop (NFEW). Five of the projects were carried out in the Summer 2020 course DIG3152 Introduction to Electronic Textual Editing, taught by Dr. Clayton McCarl of the Department of Languages, Literatures and Cultures. Emilia Thom undertook the sixth project separately in fulfillment of her capstone requirement for the Hicks Honors College. We worked to edit and encode transcriptions of items from various archives, including UNF Special Collections, the St. Augustine Historical Society, and the PK Yonge Library in Gainesville. The goals of these projects are to expand the historical narrative of northeast Florida and make the documents more accessible to academic communities and the general public. We each provide a summary of our work, along with brief reflections on what we have learned. We presented these projects at the 2021 conference of the National Council on Public History, as part of a session titled “Digital Editing as Public History Pedagogy,” organized by Dr. McCarl and Dr. Karen Cousins, director of the UNF Office of Undergraduate Research. Our editions, with exhibits that provide additional context, are available on the NFEW website (nfew.org)