966 research outputs found

    Process of Fingerprint Authentication using Cancelable Biohashed Template

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    Template protection using cancelable biometrics prevents data loss and hacking stored templates, by providing considerable privacy and security. Hashing and salting techniques are used to build resilient systems. Salted password method is employed to protect passwords against different types of attacks namely brute-force attack, dictionary attack, rainbow table attacks. Salting claims that random data can be added to input of hash function to ensure unique output. Hashing salts are speed bumps in an attacker’s road to breach user’s data. Research proposes a contemporary two factor authenticator called Biohashing. Biohashing procedure is implemented by recapitulated inner product over a pseudo random number generator key, as well as fingerprint features that are a network of minutiae. Cancelable template authentication used in fingerprint-based sales counter accelerates payment process. Fingerhash is code produced after applying biohashing on fingerprint. Fingerhash is a binary string procured by choosing individual bit of sign depending on a preset threshold. Experiment is carried using benchmark FVC 2002 DB1 dataset. Authentication accuracy is found to be nearly 97\%. Results compared with state-of art approaches finds promising

    Computational techniques to interpret the neural code underlying complex cognitive processes

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    Advances in large-scale neural recording technology have significantly improved the capacity to further elucidate the neural code underlying complex cognitive processes. This thesis aimed to investigate two research questions in rodent models. First, what is the role of the hippocampus in memory and specifically what is the underlying neural code that contributes to spatial memory and navigational decision-making. Second, how is social cognition represented in the medial prefrontal cortex at the level of individual neurons. To start, the thesis begins by investigating memory and social cognition in the context of healthy and diseased states that use non-invasive methods (i.e. fMRI and animal behavioural studies). The main body of the thesis then shifts to developing our fundamental understanding of the neural mechanisms underpinning these cognitive processes by applying computational techniques to ana lyse stable large-scale neural recordings. To achieve this, tailored calcium imaging and behaviour preprocessing computational pipelines were developed and optimised for use in social interaction and spatial navigation experimental analysis. In parallel, a review was conducted on methods for multivariate/neural population analysis. A comparison of multiple neural manifold learning (NML) algorithms identified that non linear algorithms such as UMAP are more adaptable across datasets of varying noise and behavioural complexity. Furthermore, the review visualises how NML can be applied to disease states in the brain and introduces the secondary analyses that can be used to enhance or characterise a neural manifold. Lastly, the preprocessing and analytical pipelines were combined to investigate the neural mechanisms in volved in social cognition and spatial memory. The social cognition study explored how neural firing in the medial Prefrontal cortex changed as a function of the social dominance paradigm, the "Tube Test". The univariate analysis identified an ensemble of behavioural-tuned neurons that fire preferentially during specific behaviours such as "pushing" or "retreating" for the animal’s own behaviour and/or the competitor’s behaviour. Furthermore, in dominant animals, the neural population exhibited greater average firing than that of subordinate animals. Next, to investigate spatial memory, a spatial recency task was used, where rats learnt to navigate towards one of three reward locations and then recall the rewarded location of the session. During the task, over 1000 neurons were recorded from the hippocampal CA1 region for five rats over multiple sessions. Multivariate analysis revealed that the sequence of neurons encoding an animal’s spatial position leading up to a rewarded location was also active in the decision period before the animal navigates to the rewarded location. The result posits that prospective replay of neural sequences in the hippocampal CA1 region could provide a mechanism by which decision-making is supported

    An Electronic and Web-Based Authentication, Identification, and Logging Management System

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    The need for participants’ performance assessments in academia and industry has been a growing concern. It has attendance, among other metrics, is a key factor in engendering a holistic approach to decision-making. For institutions or organizations where managing people is an important yet challenging task, attendance tracking and management could be employed to improve this seemingly time-consuming process while keeping an accurate attendance record. The manual/quasi-analog approach of taking attendance in some institutions could be unreliable and inefficient, leading to inaccurate computation of attendance rates and data loss. This work, therefore, proposes a system that employs embedded technology and a biometric/ web-based application to enhance attendance management. The hardware encompasses the integration of an ESP8266 NodeMCU and the biometric AS608 fingerprint sensor interfaced with the database for which the front-end and back-end integration was through the React framework. The system uses a web application that displays the attendance results and scheduled course data from its database, provides feedback to lecturers on which student missed classes and calculates a student’s average attendance for the semester. By implementing this system, the accuracy of student attendance is expected to experience an appreciable improvement, eliminate proxy signing, provide a more secure alternative for attendance recording, curtail tardiness to classes, and ultimately improve student productivity on execution

    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

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    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    Age-Adaptive Multimodal Biometric Authentication System with Blockchain-based Re-Enrollment

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    In the long run, a significant time gap between enrollment and probe image challenges the model's prediction ability when it has been trained on variant biometric traits. Since variant biometric traits change over time, it is sensible to construct a multimodal biometric authentication system that must include at least one invariant trait, such as the iris. The emergence of Deep learning has enabled developers to build classifiers on synthesized age-progressive images, particularly face images, to search for individuals who have been missing for many years, to avail a comprehensive portrayal of their appearance. However, in sensitive areas such as the military and banks, where security and confidentiality are of utmost importance, models should be built using real samples, and any variations in biometric traits should trigger an alert for the system and notify the subject about re-enrollment. This paper proposes an algorithm for age adaptation of biometric classifiers using multimodal channels which securely update the biometric traits while logging the transactions on the blockchain. It emphasizes confidence-score-based re-enrolment of individual subjects when the authenticator module becomes less effective with a particular subject's probe image. This reduces the time, cost, and memory involved in periodic re-enrolment of all subjects. The classifier deployed on the blockchain invokes appropriate smart contracts and completes this process securely

    Investigating the role of enhancer-mediated gene expression in the human brain and its potential contribution to psychiatric disorders

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    Autism spectrum disorder (ASD) and schizophrenia (SCZ) are two neuropsychiatric conditions with variable times of onset and are influenced by both genetic and environmental factors. Genome-wide association studies (GWASs) have led to the identification of numerous genetic loci common to both these disorders, however our understanding remains far from complete, with many clinical cases without a genetic cause. While increasing the statistical power of genome-wide association studies (GWASs) to find additional risk variants could rule-in or rule out rare cases of ASD and SCZ, this presently remains a difficult task. Furthermore, the biological functions for genetic susceptibility loci remains poorly understood, particularly for more-recent discoveries of loci devoid of gene bodies. On the other hand, recent biotechnological developments have made it possible to conduct high-resolution experimental measurements of the three-dimensional architecture of the genome, including enhancer-promoter interactions (EPIs). Such data have been used to connect GWAS risk variants to their potential target genes which, in turn, provide insights into underlying molecular mechanisms and cellular processes. The functions of enhancer-promoter interactions in controlling gene expression programmes is crucial to how implicated genes mediate neurological function and disease. Yet, knowledge on enhancer-promoter interactions remains to be used in conjunction with GWAS data, particularly on such data from specific brain cell types, which may be useful to uncover the biological underpinnings of psychiatric conditions. This thesis examines the role of enhancer-mediated gene expression in the human brain and its potential contribution to psychiatric conditions. In Chapter 2, I report on the identification of significant chromosomal interactions from studies of brain Hi-C data generated from neuronal and glial cells, with the goal to investigate the impact of EPIs genome-wide, as well as to provide a template for an in-depth understanding of how EPIs impact transcriptional regulation. In the Chapter 3, I discuss a novel approach integrating Activity by Contact (ABC) and gene set enrichment analyses of GWAS data in two steps. In the first step, ABC is used to predict enhancer-gene regulatory interactions in a given cell type (e.g., glial cells, neurons). Secondly, Hi-C coupled multi-marker analysis of genomic annotation (H-MAGMA) is used to assign the SNPs located in the regulatory regions identified by ABC to each gene and calculate gene-level association p-values. I applied this novel framework (ABC-HMAGMA) to GWAS data from SCZ and ASD, to identify novel SCZ and ASD trait-associated genes and molecular pathways. In Chapter 4, I have evaluated a potential novel mechanism for the regulation of enhancer activity within cells. I hypothesized that, in addition to its known roles in DNA replication and transcription, Topoisomerase I may regulate enhancer activity in brain cells. To test this hypothesis, I employed RNA-seq and transient transcriptome sequencing (TT-seq) data, a method that enriches for short-lived enhancer derived RNAs. These data showed that Topoisomerase I inhibition leads to significant changes in eRNA expression and offers evidence that such changes are relevant to the homeostatic functions for Top 1 in cellular gene expression regulation

    Mobility classification of cattle with micro-Doppler radar

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    Lameness in dairy cattle is a welfare concern that negatively impacts animal productivity and farmer profitability. Micro-Doppler radar sensing has been previously suggested as a potential system for automating lameness detection in ruminants. This thesis investigates the refinement of the proposed automated system by analysing and enhancing the repeatability and accuracy of the existing scoring method in cattle mobility scoring, used to provide labels in machine learning. The main aims of the thesis were (1) to quantify the performance of the micro-Doppler radar sensing method for the assessment of mobility, (2) to characterise and validate micro-Doppler radar signatures of dairy cattle with varying degrees of gait impairment, and (3) to develop machine learning algorithms that can infer the mobility status of the animals under test from their radar signatures and support automatic contactless classification. The first study investigated inter-assessor agreement using a 4-level system and modifications to it, as well as the impact of factors such as mobility scoring experience, confidence in scoring decisions, and video characteristics. The results revealed low levels of agreement between assessors' scores, with kappa values ranging from 0.16 to 0.53. However, after transforming and reducing the mobility scoring system levels, an improvement was observed, with kappa values ranging from 0.2 to 0.67. Subsequently, a longitudinal study was conducted using good-agreement scores as ground truth labels in supervised machine-learning models. However, the accuracy of the algorithmic models was found to be insufficient, ranging from 0.57 to 0.63. To address this issue, different labelling systems and data pre-processing techniques were explored in a cross-sectional study. Nonetheless, the inter-assessor agreement remained challenging, with an average kappa value of 0.37 (SD = 0.16), and high-accuracy algorithmic predictions remained elusive, with an average accuracy of 56.1 (SD =16.58). Finally, the algorithms' performance was tested with high-confidence labels, which consisted of only scores 0 and 3 of the AHDB system. This testing resulted in good classification accuracy (0.82), specificity (0.79), and sensitivity (0.85). This led to the proposal of a new approach to producing labels, testing vantage point changes, and improving the performance of machine learning models (average accuracy = 0.7 & SD = 0.17, average sensitivity = 0.68 & SD = 0.27, average specificity = 0.75 & SD = 0.17). The research identified a challenge in creating high-confidence diagnostic labels for supervised machine learning-based algorithms to automate the detection and classification of lameness in dairy cows. As a result, the original goals were partially overridden, with the focus shifted to creating reliable labels that would perform well with radar data and machine learning. This point was considered necessary for smooth system development and process automation. Nevertheless, we managed to quantify the performance of the micro-Doppler radar system, partially develop the supervised machine learning algorithms, compare levels of agreement among multiple assessors, evaluate the assessment tools, assess the mobility evaluation process and gather a valuable data set which can be used as a foundation for subsequent studies. Finally, the thesis suggests changes in the assessment process to improve the prediction accuracy of algorithms based on supervised machine learning with radar data

    Intelligent interface agents for biometric applications

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    This thesis investigates the benefits of applying the intelligent agent paradigm to biometric identity verification systems. Multimodal biometric systems, despite their additional complexity, hold the promise of providing a higher degree of accuracy and robustness. Multimodal biometric systems are examined in this work leading to the design and implementation of a novel distributed multi-modal identity verification system based on an intelligent agent framework. User interface design issues are also important in the domain of biometric systems and present an exceptional opportunity for employing adaptive interface agents. Through the use of such interface agents, system performance may be improved, leading to an increase in recognition rates over a non-adaptive system while producing a more robust and agreeable user experience. The investigation of such adaptive systems has been a focus of the work reported in this thesis. The research presented in this thesis is divided into two main parts. Firstly, the design, development and testing of a novel distributed multi-modal authentication system employing intelligent agents is presented. The second part details design and implementation of an adaptive interface layer based on interface agent technology and demonstrates its integration with a commercial fingerprint recognition system. The performance of these systems is then evaluated using databases of biometric samples gathered during the research. The results obtained from the experimental evaluation of the multi-modal system demonstrated a clear improvement in the accuracy of the system compared to a unimodal biometric approach. The adoption of the intelligent agent architecture at the interface level resulted in a system where false reject rates were reduced when compared to a system that did not employ an intelligent interface. The results obtained from both systems clearly express the benefits of combining an intelligent agent framework with a biometric system to provide a more robust and flexible application
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