CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting Authentication

Handwriting authentication is a valuable tool used in various fields, such as fraud prevention and cultural heritage protection. However, it remains a challenging task due to the complex features, severe damage, and lack of supervision. In this paper, we propose a novel Contrastive Self-Supervised Learning framework for Robust Handwriting Authentication (CSSL-RHA) to address these issues. It can dynamically learn complex yet important features and accurately predict writer identities. Specifically, to remove the negative effects of imperfections and redundancy, we design an information-theoretic filter for pre-processing and propose a novel adaptive matching scheme to represent images as patches of local regions dominated by more important features. Through online optimization at inference time, the most informative patch embeddings are identified as the "most important" elements. Furthermore, we employ contrastive self-supervised training with a momentum-based paradigm to learn more general statistical structures of handwritten data without supervision. We conduct extensive experiments on five benchmark datasets and our manually annotated dataset EN-HA, which demonstrate the superiority of our CSSL-RHA compared to baselines. Additionally, we show that our proposed model can still effectively achieve authentication even under abnormal circumstances, such as data falsification and corruption.Comment: 10 pages, 4 figures, 3 tables, submitted to ACM MM 202

Draft Regional Recommendations for the Pacific Northwest on Water Quality Trading

In March 2013, water quality agency staff from Idaho, Oregon, and Washington, U.S. EPA Region 10, Willamette Partnership, and The Freshwater Trust convened a working group for the first of a series of four interagency workshops on water quality trading in the Pacific Northwest. Facilitated by Willamette Partnership through a USDA-NRCS Conservation Innovation Grant, those who assembled over the subsequent eight months discussed and evaluated water quality trading policies, practices, and programs across the country in an effort to better understand and draw from EPA's January 13, 2003, Water Quality Trading Policy, and its 2007 Permit Writers' Toolkit, as well as existing state guidance and regulations on water quality trading. All documents presented at those conversations and meeting summaries are posted on the Willamette Partnership's website.The final product is intended to be a set of recommended practices for each state to consider as they develop water quality trading. The goals of this effort are to help ensure that water quality "trading programs" have the quality, credibility, and transparency necessary to be consistent with the "Clean Water Act" (CWA), its implementing regulations and state and local water quality laws

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

Learning the language of academic engineering: Sociocognitive writing in graduate students

Although engineering graduate programs rarely require academic writing courses, the indicators of merit in academic engineering, such as journal publications, successful grants, and doctoral milestones (e.g. theses, dissertations) are based in effective written argumentation and disciplinary discourse. Further, graduate student attrition averages 57% across all disciplines, with some studies classifying up to 50% of these students as “ABD” (All But Dissertation.) In engineering disciplines specifically, graduate attrition rates across the U.S. average 36% (both Master’s and PhD students), according to the Council of Graduate Schools. The lack of socialization is generally noted as a main reason for graduate attrition, one of the primary elements of which is the development of disciplinary identity and membership within a discourse community. To this end, this research presents findings from a mixed methods study that maps the writing attitudes, processes and dispositions of engineering graduate students with enacted writing patterns in research proposals. Statistical survey data and the research proposals from 50 winners of the National Science Foundation’s Graduate Research Fellowship Program (NSF GRFP) were analyzed through statistical methods, genre analysis, and content analysis methods. Interpreted through Role Identity Theory and Academic Literacies Theory, the findings from this research indicate that engineering writers may approach writing differently from students in other disciplines, and as such, the instruction of engineering writing should be taught in ways that encourage sociocognitive enculturation of graduate students into the engineering discourse community

A Checkout Language for Future Space Vehicles

To support an increased emphasis on automated checkout of future space vehicles, a procedureoriented computer language is required. This language needs to be more user-oriented and needs to have a more complete set of capabilities than existing languages. Such a language, named TOTAL, was developed under contract to NASAKSC. This paper presents an overall view of the language in terms of its major characteristics as derived from the basic design objectives

HSP90 inhibitors stimulate DNAJB4 protein expression through a mechanism involving N6-methyladenosine.

Small-molecule inhibitors for the 90-kDa heat shock protein (HSP90) have been extensively exploited in preclinical studies for the therapeutic interventions of human diseases accompanied with proteotoxic stress. By using an unbiased quantitative proteomic method, we uncover that treatment with three HSP90 inhibitors results in elevated expression of a large number of heat shock proteins. We also demonstrate that the HSP90 inhibitor-mediated increase in expression of DNAJB4 protein occurs partly through an epitranscriptomic mechanism, and is substantially modulated by the writer, eraser, and reader proteins of N6-methyladenosine (m6A). Furthermore, exposure to ganetespib leads to elevated modification levels at m6A motif sites in the 5'-UTR of DNAJB4 mRNA, and the methylation at adenosine 114 site in the 5'-UTR promotes the translation of the reporter gene mRNA. This m6A-mediated mechanism is also at play upon heat shock treatment. Cumulatively, we unveil that HSP90 inhibitors stimulate the translation of DNAJB4 through an epitranscriptomic mechanism

Automatic intrapersonal variability modeling for offline signature augmentation

Orientador: Luiz Eduardo Soares de OliveiraCoorientadores: Robert Sabourin e Alceu de Souza Britto Jr..Tese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 19/07/2021Inclui referências: p. 93-102Área de concentração: Ciência da ComputaçãoResumo: Normalmente, em um cenario do mundo real, poucas assinaturas estao disponiveis para treinar um sistema de verificacao automatica de assinaturas (SVAA). Para resolver esse problema, diversas abordagens para a duplicacao de assinaturas estaticas foram propostas ao longo dos anos. Essas abordagens geram novas amostras de assinaturas sinteticas aplicando algumas transformacoes na imagem original da assinatura. Algumas delas geram amostras realistas, especialmente o duplicator. Este metodo utiliza um conjunto de parametros para modelar o comportamento do escritor (variabilidade do escritor) ao assinar. No entanto, esses parametros so empiricamente definidos. Este tipo de abordagem pode ser demorado e pode selecionar parametros que nao descrevem a real variabilidade do escritor. A principal hipotese desse trabalho e que a variabilidade do escritor observada no dominio da imagem tambem pode ser transferido para o dominio de caracteristicas. Portanto, este trabalho propoe um novo metodo para modelar automaticamente a variabilidade do escritor para a posterior duplicacao de assinaturas no dominio de imagem (duplicator) e dominio de caracteristicas (filtro Gaussiano e variacao do metodo de Knop). Este trabalho tambem propoe um novo metodo de duplicacao de assinaturas estaticas, que gera as amostras sinteticas diretamente no dominio de caracteristicas usando um filtro Gaussiano. Alem disso, uma nova abordagem para avaliar a qualidade de amostras sinteticas no dominio de caracteristicas e apresentada. As limitacoes e vantagens de ambas as abordagens de duplicacao de assinaturas tambem sao exploradas. Alem de usar a nova abordagem para avaliar a qualidade das amostras, o desempenho de um SVAA e avaliado usando as amostras e tres bases de assinaturas estaticas bem conhecidas: a GPDS-300, a MCYT-75 e a CEDAR. Para a mais utilizada, GPDS-300, quando o classificador SVM foi treinando com somente uma assinatura genuina por escritor, ele obteve um Equal Error Rate (EER) de 5,71%. Quando o classificador tambem utilizou as amostras sinteticas geradas no dominio de imagem, o EER caiu para 1,08%. Quando o classificador foi treinado com as amostras geradas pelo filtro Gaussiano, o EER caiu para 1,04%.Abstract: Normally, in a real-world scenario, there are few signatures available to train an automatic signature verification system (ASVS). To address this issue, several offline signature duplication approaches have been proposed along the years. These approaches generate a new synthetic signature sample applying some transformations in the original signature image. Some of them generate realistic samples, specially the duplicator. This method uses a set of parameters to model the writer's behavior (writer variability) during the signing act. However, these parameters are empirically defined. This kind of approach can be time consuming and can select parameters that do not describe the real writer variability. The main hypothesis of this work is that the writer variability observed in the image space can be transferred to the feature space as well. Therefore, this work proposes a new method to automatically model the writer variability for further signature duplication in the image (duplicator) and the feature space (Gaussian filter and a variation of Knop's method). This work also proposes a new offline signature duplication method, which directly generates the synthetic samples in the feature space using a Gaussian filter. Furthermore, a new approach to assess the quality of the synthetic samples in the feature space is introduced. The limitations and advantages of both signature augmentation approaches are also explored. Despite using the new approach to assess the quality of the samples, the performance of an ASVS was assessed using them and three well-known offline signature datasets: GPDS-300, MCYT-75, and CEDAR. For the most used one, GPDS-300, when the SVM classifier was trained with only one genuine signature per writer, it achieved an Equal Error Rate (EER) of 5.71%. When the classifier also was trained with the synthetic samples generated in the image space, the EER dropped to 1.08%. When the classifier was trained using the synthetic samples generated by the Gaussian filter, the EER dropped to 1.04%

A Novel Method to Detect Segmentation points of Arabic Words using Peaks and Neural Network

Many methods of segmentation using detection of segmentation points or where the location of segmentation points is expected before the segmentation process,  the validity of segmentation points is verified by using ANNs. In this paper apply a novel method to detect correctly of location segmentation points by detect of peaks with neural networks for Arabic word. This method employs baseline and peaks identification; where using two steps to segmenting text. Where peaks identification function is applied which at the subword segment level to frame the minimum and maximum peaks, and baseline detection. Where these two steps have led to the best result through the model depends on minimum peaks attained by utilising a stroke operator with a view to extracting potential points of segmentation, and determining the baseline procedure was developed to approximate the parameters. Where this method has yielded highly accurate positive results for Arabic characters’ segmentation with four kinds of handwritten datasets as AHDB, IFN-ENIT, AHDB-FTR and ACDAR. Earlier results showed that the use of EDMS to MLP_ANN gives better results than GLCM and MOMENT in different groups and gives results of EDMS features on MNN with an accuracy level of 95.09% classifier for IFN-ENIT set of data

The Influence of Language Choice in Acceptable Use Polices On Students’ Locus of Control

One of the goals of education is for students to develop critical thinking skills. In order to build those skills, students must become critical and engaged users of information. Students become engaged and critical users of information when they have opportunities to explore and immerse themselves in information from different viewpoints and perspectives. Much of the information accessed by students today is located online. In many school districts, an Acceptable Use Policy (AUP) details what type of access students have to information found online. Using Rotter’s Locus of Control Theory, this study seeks to answer the question of how language choice in AUPs influences students’ Locus of Control. Previous studies on Locus of Control have demonstrated that students who identify with an external Locus of Control believe that powerful others control their lives. To answer the question, Critical Discourse Analysis (CDA) was utilized to analyze AUPs from eighteen public school districts in the Midwest. CDA is a methodology used to study social inequality through the assertion of power in written communication. The AUPs were analyzed for word choice, frequency, presupposition, and nominalization. Results of the analysis demonstrated that language choices have the potential to influence students’ Locus of iii Control through the assertion of power. Thus, language in AUPs, which asserts power over students, has the potential to create a restrictive information environment for students. A more restrictive information environment will limit opportunities for students to access diverse information whereas a more open information environment will allow students to question and develop their critical thinking skills

Creating Memories: Writing and Designing More Memorable Documents

If communication’s purpose is to enable action or belief (Johnson-Sheehan, 2012), then communication will be more effective—and thus more ethical—if the audience can easily remember it. However, the study of memory has long been neglected in English Studies. Therefore, communicators lack strategies for enhancing documents’ memorableness and an ethical framework for assessing (un)memorable documents and composing processes. To develop an “ethic of memory” and identify strategies that enhance a document’s memorableness, I asked twenty subjects—ten teachers and ten college freshman—to walk down a high school hallway in which various posters and flyers had been posted by the administration, teachers, or students. Then I interviewed the subjects about their recollections, reasons for remembering this information, and the likelihood that they might apply it. One week later, I conducted a follow-up interview to determine which information “stuck,” the subjects’ self-reported reasons why, and their likelihood of applying it. I counted the number of information units and specific details that the subjects remembered at each interview, and I also categorized the types of details they recalled. I coded the subjects’ reasons for remembering and (not) applying information according to commonly-accepted design and psychological terms drawn from Universal Principles of Design by Lidwell et al. The subjects’ memories were very consistent in both quantity and quality from the first to the second interview, indicating that documents influence long-term memory. Certain posters and flyers were remembered much more often than others, demonstrating that rhetorical and design strategies affect a documents’ memorableness. The codes “schema” and “relevance” were very consistent themes in the subjects’ interview responses; so-called “self-schema” shape judgments of relevance, which then affect efforts to encode information into memory. This study describes six strategies for engaging an audience’s collective self-schema, prompting the audience to ascribe relevance to documents and thus endeavor to encode them: convey practical value; use the familiar; use contrast, color, and imagery; use unexpected elements; arouse emotion and build social currency; and “break-and-remake” existing schema