Multiple generation of Bengali static signatures

Handwritten signature datasets are really necessary for the purpose of developing and training automatic signature verification systems. It is desired that all samples in a signature dataset should exhibit both inter-personal and intra-personal variability. A possibility to model this reality seems to be obtained through the synthesis of signatures. In this paper we propose a method based on motor equivalence model theory to generate static Bengali signatures. This theory divides the human action to write mainly into cognitive and motor levels. Due to difference between scripts, we have redesigned our previous synthesizer [1,2], which generates static Western signatures. The experiments assess whether this method can approach the intra and inter-personal variability of the Bengali-100 Static Signature DB from a performance-based validation. The similarities reported in the experimental results proof the ability of the synthesizer to generate signature images in this script

Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)

[Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”. Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità, e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”. A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine. The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art. The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics

Generational perspective on asthma self-management in the Bangladeshi and Pakistani community in the United Kingdom: A qualitative study

BACKGROUND: Self‐management strategies improve asthma outcomes, although interventions for South Asian populations have been less effective than in White populations. Both self‐management and culture are dynamic, and factors such as acculturation and generation have not always been adequately reflected in existing cultural interventions. We aimed to explore the perspectives of Bangladeshi and Pakistani people in the United Kingdom, across multiple generations (first, second and third/fourth), on how they self‐manage their asthma, with a view to suggesting recommendations for cultural interventions. METHODS: We purposively recruited Bangladeshi and Pakistani participants, with an active diagnosis of asthma from healthcare settings. Semi‐structured interviews in the participants' choice of language (English, Sylheti, Standard Bengali or Urdu) were conducted, and data were analysed thematically. RESULTS: Twenty‐seven participants (13 Bangladeshi and 14 Pakistani) were interviewed. There were generational differences in self‐management, influenced by complex cultural processes experienced by South Asians as part of being an ethnic minority group. Individuals from the first generation used self‐management strategies congruent to traditional beliefs such as ‘sweating’ and often chose to travel to South Asian countries. Generations born and raised in the United Kingdom learnt and experimented with self‐management based on their fused identities and modified their approach depending on whether they were in familial or peer settings. Acculturative stress, which was typically higher in first generations who had migration‐related stressors, influenced the priority given to asthma self‐management throughout generations. The amount and type of available asthma information as well as social discussions within the community and with healthcare professionals also shaped asthma self‐management. CONCLUSIONS: Recognizing cultural diversity and its influence of asthma self‐management can help develop effective interventions tailored to the lives of South Asian people. PATIENT OR PUBLIC CONTRIBUTION: Patient and Public Involvement colleagues were consulted throughout to ensure that the study and its materials were fit for purpose

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%

Whole-genome sequencing for an enhanced understanding of genetic variation among South Africans

The Southern African Human Genome Programme is a national initiative that aspires to unlock the unique genetic character of southern African populations for a better understanding of human genetic diversity. In this pilot study the Southern African Human Genome Programme characterizes the genomes of 24 individuals (8 Coloured and 16 black southeastern Bantu-speakers) using deep whole-genome sequencing. A total of ~16 million unique variants are identified. Despite the shallow time depth since divergence between the two main southeastern Bantu-speaking groups (Nguni and Sotho-Tswana), principal component analysis and structure analysis reveal significant (p < 10−6) differentiation, and FST analysis identifies regions with high divergence. The Coloured individuals show evidence of varying proportions of admixture with Khoesan, Bantu-speakers, Europeans, and populations from the Indian sub-continent. Whole-genome sequencing data reveal extensive genomic diversity, increasing our understanding of the complex and region-specific history of African populations and highlighting its potential impact on biomedical research and genetic susceptibility to disease

Shabag, a critical social moment: a collective agency capabilities analysis

This thesis sets out an approach to understanding the impact of change oriented ‘social moments’ on social practices and structures. The empirical case on which the thesis draws to develop this argument is the Shahbag protests in Bangladesh. At the theoretical level, the thesis suggests that ‘Social Moments’ oriented to change (as differentiated from social movements) can be triggered by latent injustices occurring within a given society. Using a Critical Theoretical lens and the Capability Approach, the thesis sets out a Being-Doing-Impact Model oriented to an understanding of the conditions necessary for a Social Moment to occur. These moments occur where individuals from different parts of the social habitus come together, to create a scene, as a critical mass in order to effect change. Such moments can lead to shifts in systems and practices, and ultimately to a more just society. The research assesses in detail the conditions that made the Shahbag Moment possible. These conditions include: the presence of the necessary agency capabilities of individuals; the effective mobilisation of instrumental freedoms; the substantive presence of networks of social support and solidarity (all of which bring into play an important affective dimension). The wider social context is also viewed as a crucial component. The thesis shows how, for example, the atmosphere at Shahbag can be considered as cultural, positive and safe. It also shows a willingness on the part of Government to listen and respond to the will of the people. Moreover, the role of the media and social media, which shared the Moment’s messages, and offered an open and transparent information platform to debate and discuss the issues was significant. An analysis of the case histories of the Shahbag Moment in Bangladesh allows for the further development of the theoretical approach in a concrete empirical contex

Learning features for offline handwritten signature verification

Handwritten signatures are the most socially and legally accepted means for identifying a person. Over the last few decades, several researchers have approached the problem of automating their recognition, using a variety of techniques from machine learning and pattern recognition. In particular, most of the research effort has been devoted to obtaining good feature representations for signatures, by designing new feature extractors, as well as experimenting with feature extractors developed for other purposes. To this end, researchers have used insights from graphology, computer vision, signal processing, among other areas. In spite of the advancements in the field, building classifiers that can separate between genuine signatures and skilled forgeries (forgeries made targeting a particular individual) is still an open research problem. In this thesis, we propose to address this problem from another perspective, by learning the feature representations directly from signature images. The hypothesis is that, in the absence of a good model of the data generation process, it is better to learn the features from data. As a first contribution, we propose a method to learn Writer-Independent features using a surrogate objective, followed by training Writer-Dependent classifiers using the learned features. Furthermore, we define an extension that allows leveraging the knowledge of skilled forgeries (from a subset of users) in the feature learning process. We observed that such features generalize well to new users, obtaining state-of-the-art results on four widely used datasets in the literature. As a second contribution, we investigate three issues of signature verification systems: (i) learning a fixed-sized vector representation for signatures of varied size; (ii) analyzing the impact of the resolution of the scanned signatures in system performance and (iii) how features generalize to new operating conditions with and without fine-tuning. We propose methods to handle signatures of varied size and our experiments show results comparable to state-of-theart while removing the requirement that all input images have the same size. As a third contribution, we propose to formulate the problem of signature verification as a meta-learning problem. This formulation also learns directly from signatures images, and allows the direct optimization of the objective (separating genuine signatures and skilled forgeries), instead of relying on surrogate objectives for learning the features. Furthermore, we show that this method is naturally extended to formulate the adaptation (training) for new users as one-class classification. As a fourth contribution, we analyze the limitations of these systems in an Adversarial Machine Learning setting, where an active adversary attempts to disrupt the system. We characterize new threats posed by Adversarial Examples on a taxonomy of threats to biometric systems, and conduct extensive experiments to evaluate the success of attacks under different scenarios of attacker’s goals and knowledge of the system under attack. We observed that both systems that rely on handcrafted features, as well as those using learned features, are susceptible to adversarial attacks in a wide range of scenarios, including partial-knowledge scenarios where the attacker does not have full access to the trained classifiers. While some defenses proposed in the literature increase the robustness of the systems, this research highlights the scenarios where such systems are still vulnerable

Rohingyas in Bangladesh: Owning Rohingya Identity in Disowning Spaces

This dissertation focuses on Rohingya people, with a special emphasis on Rohingya youth and young adults, and how they construct their identities. While Rohingya ethnic identity is deeply rooted in Burma, it is influenced by how they grow up and reach adulthood within a protracted situation in Bangladesh. Many Rohingya youth and young adults find it complicated to define who they are because they belong to a place, Burma, that does not consider them citizens, and they reside in a place, Bangladesh, that never recognizes them as residents. The uncertainty around Rohingya identity raises several questions: How does the experience of displacement and refugeeness in Bangladesh shape identity among Rohingya people, particularly among the youth and young adults? What is Rohingya identity? In what ways do they retain their Rohingya identity in the context of their non-citizen status in Bangladesh? While they are stateless, how are the social rights of citizenship experienced by Rohingya people? Using ethnographic methods, I spent nine months in Coxs Bazar, Bangladesh, between 2014 and 2016 to collect data for this research. I interviewed 44 Rohingya people. Rohingyas first arrived in Bangladesh in 1978. After that, many Rohingya people were born and/or raised in Bangladeshi refugee camps, and have never left, while others were forcefully repatriated by Bangladeshi government and then forced to return to Bangladesh again by the Burmese government during 1992-1993 (Abrar, 1995; Pittaway, 2008; Loescher & Milner, 2008; Ullah, 2011; Murshid, 2014). The findings of my research show that due to living in oppressive conditions, uncertainty, and the lack of an appropriate social environment, Rohingya people struggle with forming their identity. Their liminality, statelessness, and lack of rights have created an unsettled and hybrid form of identity for many youth and youth adults living within and outside the refugee camps. In this dissertation, I first describe the lives of Rohingya refugees, then I examine individual constructions of identity and how their sense of belonging is influenced by their refugeeness and lack of legal citizenship. Rohingya peoples struggle with identity formation can only be resolved when the Rohingya crisis comes to an end