Bayesian hierarchical modeling for the forensic evaluation of handwritten documents

The analysis of handwritten evidence has been used widely in courts in the United States since the 1930s (Osborn, 1946). Traditional evaluations are conducted by trained forensic examiners. More recently, there has been a movement toward objective and probability-based evaluation of evidence, and a variety of governing bodies have made explicit calls for research to support the scientific underpinnings of the field (National Research Council, 2009; President\u27s Council of Advisors on Science and Technology (US), 2016; National Institutes of Standards and Technology). This body of work makes contributions to help satisfy those needs for the evaluation of handwritten documents. We develop a framework to evaluate a questioned writing sample against a finite set of genuine writing samples from known sources. Our approach is fully automated, reducing the opportunity for cognitive biases to enter the analysis pipeline through regular examiner intervention. Our methods are able to handle all writing styles together, and result in estimated probabilities of writership based on parametric modeling. We contribute open-source datasets, code, and algorithms. A document is prepared for the evaluation processed by first being scanned and stored as an image file. The image is processed and the text within is decomposed into a sequence of disjoint graphical structures. The graphs serve as the smallest unit of writing we will consider, and features extracted from them are used as data for modeling. Chapter 2 describes the image processing steps and introduces a distance measure for the graphs. The distance measure is used in a K-means clustering algorithm (Forgy, 1965; Lloyd, 1982; Gan and Ng, 2017), which results in a clustering template with 40 exemplar structures. The primary feature we extract from each graph is a cluster assignment. We do so by comparing each graph to the template and making assignments based on the exemplar to which each graph is most similar in structure. The cluster assignment feature is used for a writer identification exercise using a Bayesian hierarchical model on a small set of 27 writers. In Chapter 3 we incorporate new data sources and a larger number of writers in the clustering algorithm to produce an updated template. A mixture component is added to the hierarchical model and we explore the relationship between a writer\u27s estimated mixing parameter and their writing style. In Chapter 4 we expand the hierarchical model to include other graph-based features, in addition to cluster assignments. We incorporate an angular feature with support on the polar coordinate system into the hierarchical modeling framework using a circular probability density function. The new model is applied and tested in three applications

Comparison of signatures on paper and graphic pad using multivariate analysis

Most sectors are progressively adopting electronic signatures as a standard business method due to technology improvements and the allure of a paperless workflow. The increasing use of electronic signatures has presented challenges for document examiners in their methods of examination due to the absence of well defined procedures, pertinent research, and differences between electronic signatures and handwritten signatures. As a first step to answer the question regarding the differences in signature writers in various mediums, 30 signatures were collected on Double-A, A4 paper using a blue Ballpoint pen Grip X10 1.0mm, and another 30 signatures on the XP-Pen Star G430 graphic pen tablet with a P01 Stylus pen from 5 subjects. Then, the similarities and differences between the signatures were analysed using three parameters that represent vertical dimension movement in writing, namely the ratio of the upper zone to the total height, the ratio of the middle zone to the total height and the ratio of the lower zone to the total height. The parameters were then tested with multiple tests. The ratios were used to calculate the mean and RSD and subsequently for statistical analysis. In summary, intra-variation among the authors was evident, regardless of the writing medium used in signatures. These suggested that natural variation was common, and no people could produce the same signature every occasion. Based on the statistical results, the K-mean clustering accuracy was at least 60% and above for all the signees. Perhaps, this shows differences in the signature written on paper and graphic pad, although the same person wrote it simultaneously. In factor analysis followed by scatter plots, the signatures analysed in this research are exhibited within and between varying writing mediums. The within variation suggested that natural variation was expected regardless of the writing medium used in signatures, and no people could produce the same signature on every occasion. The variation between writing mediums, namely paper and graphic pad, is evident, although the same person signs it. Therefore, the null hypothesis is rejected, and the alternative hypothesis is accepted where the signature from the same person will be significantly different when using different writing mediums

Using AI to solve business problems in scholarly publishing

Artificial intelligence (AI) tools are widely used today in many areas, and are now being introduced into scholarly publishing. This article provides a brief overview of present-day AI and machine learning as used for text-based resources such as journal articles and book chapters, and provides an example of its application to identify suitable peer reviewers for manuscript submissions. It describes how one company, UNSILO, has created a tool for this purpose, and the underlying technology used to deliver it. The article also offers a glimpse into a future where AI will profoundly change the way that academic publishing will work

2023 SDSU Data Science Symposium Presentation Abstracts

This document contains abstracts for presentations and posters 2023 SDSU Data Science Symposium

2023 SDSU Data Science Symposium Presentation Abstracts

This document contains abstracts for presentations and posters 2023 SDSU Data Science Symposium

Scientific Development of an Integrated Workflow for Latent Print, Questioned Document, and DNA Processing of Paper Evidence

Touch paper evidence could be the source of probative human DNA but recovery is challenging and forensic laboratories instead prioritize processing by the Latent Print and Questioned Document disciplines. Recent advances in DNA collection methods and the increased sensitivity of STR typing kits have improved success rates for DNA testing of paper evidence; but prior to implementing DNA collection, laboratories have to decide in which order to examine paper for the different types of forensic evidence. This thesis developed and tested a multi-discipline workflow for processing paper evidence by DNA, Latent Prints and Questioned Documents experts. Preliminary sampling studies indicated swabbing twice with a dry swab and vacuuming the surface were comparable in DNA recovery and did not impact the subsequent paper evidence results for Latent Prints or Questioned Documents. Improved quality of detected prints was observed with 1,2- indanedione zinc chloride treatment. DNA swabbing of the paper and/or EDD film during Questioned Document processing did not improve DNA yields. The proposed paper evidence workflow of DNA processing followed by Questioned Document processing, and Latent Print processing was tested on handwritten notes from a variety of donors and on different types of paper. Large sheets of paper, like copy and notepad paper, yielded between 67% and 92% interpretable DNA profiles. Controlled indented impressions and latent prints of value were detected as expected following DNA processing, validating the workflow. The project also evaluated the stability of DNA deposited on paper by touching and showed that DNA remained stable over a twelve-month span

The Proficiency of Experts

Expert evidence plays a crucial role in civil and criminal litigation. Changes in the rules concerning expert admissibility, following the Supreme Court\u27s Daubert ruling, strengthened judicial review of the reliability and the validity of an expert\u27s methods. Judges and scholars, however, have neglected the threshold question for expert evidence: whether a person should be qualified as an expert in the first place. Judges traditionally focus on credentials or experience when qualifying experts without regard to whether those criteria are good proxies for true expertise. We argue that credentials and experience are often poor proxies for proficiency. Qualification of an expert presumes that the witness can perform in a particular domain with a proficiency that non-experts cannot achieve, yet many experts cannot provide empirical evidence that they do in fact perform at high levels of proficiency. To demonstrate the importance ofproficiency data, we collect and analyze two decades of proficiency testing of latent fingerprint examiners. In this important domain, we found surprisingly high rates of false positive identifications for the period 1995 to 2016. These data would qualify the claims of many fingerprint examiners regarding their near infallibility, but unfortunately, judges do not seek out such information. We survey the federal and state case law and show how judges typically accept expert credentials as a proxy for proficiency in lieu of direct proof of proficiency. Indeed, judges often reject parties\u27 attempts to obtain and introduce at trial empirical data on an expert\u27s actual proficiency. We argue that any expert who purports to give falsifiable opinions can be subjected to proficiency testing and that proficiency testing is the only objective means of assessing the accuracy and reliability ofexperts who rely on subjective judgments to formulate their opinions (so-called black-box experts ). Judges should use proficiency data to make expert qualification decisions when the data is available, should demand proof of proficiency before qualifying black-box experts, and should admit at trial proficiency data for any qualified expert. We seek to revitalize the standard for qualifying experts: expertise should equal proficiency

The path to improving the quality of laboratory documentation : (a case study from Cameroon)

Health care systems nowadays are affected by quality problems, most of which occur in developing countries due to the lack of adequate infrastructural, human, and financial resources. This has also caused the data quality generated in developing countries to be often poor. As a result, most governments in developing countries are in the process of improving quality in their health care systems through the introduction of Information Technology (IT) support systems. This thesis explored the challenges and opportunities involved in the path to improving the quality of laboratory documentation in a Cameroonian hospital. The study employed the qualitative research approach whereby interpretive research methods were used during data collection. These consisted of participant observations, interviews, and document analysis. A total of 24 respondents were interviewed comprising of 19 hospital staff and 5 patients. The data was collected at the medical laboratory department of the Regional Hospital Bamenda over a period of two months. The theories of Information Infrastructures and Actor Network guided the study, that is, they were used to discuss the laboratory documentation, and the implementation of the IT support system in the everyday work practice. The study findings primarily revealed certain quality-related lapses in the laboratory documentation. For example, illegible laboratory test orders, common errors in laboratory test ordering and result reporting, just to name a few. It further revealed that IT support systems have great potential to improve upon the quality of the laboratory documentation. Thus, it suggested that a tailored IT support system could be implemented to address this issue. However, the greatest challenge discovered was the lack of resources to make this happen. Based on these findings, it was suggested that if resources are made available to implement this system, the socio-technical approach should be employed in order to ensure success. This is because this approach has proven to be effective since it does not only take into consideration the new technology implemented, but also the interaction between the technology and its users

Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/