Advanced document data extraction techniques to improve supply chain performance

In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information

Exploiting Wikipedia Semantics for Computing Word Associations

Semantic association computation is the process of automatically quantifying the strength of a semantic connection between two textual units based on various lexical and semantic relations such as hyponymy (car and vehicle) and functional associations (bank and manager). Humans have can infer implicit relationships between two textual units based on their knowledge about the world and their ability to reason about that knowledge. Automatically imitating this behavior is limited by restricted knowledge and poor ability to infer hidden relations. Various factors affect the performance of automated approaches to computing semantic association strength. One critical factor is the selection of a suitable knowledge source for extracting knowledge about the implicit semantic relations. In the past few years, semantic association computation approaches have started to exploit web-originated resources as substitutes for conventional lexical semantic resources such as thesauri, machine readable dictionaries and lexical databases. These conventional knowledge sources suffer from limitations such as coverage issues, high construction and maintenance costs and limited availability. To overcome these issues one solution is to use the wisdom of crowds in the form of collaboratively constructed knowledge sources. An excellent example of such knowledge sources is Wikipedia which stores detailed information not only about the concepts themselves but also about various aspects of the relations among concepts. The overall goal of this thesis is to demonstrate that using Wikipedia for computing word association strength yields better estimates of humans' associations than the approaches based on other structured and unstructured knowledge sources. There are two key challenges to achieve this goal: first, to exploit various semantic association models based on different aspects of Wikipedia in developing new measures of semantic associations; and second, to evaluate these measures compared to human performance in a range of tasks. The focus of the thesis is on exploring two aspects of Wikipedia: as a formal knowledge source, and as an informal text corpus. The first contribution of the work included in the thesis is that it effectively exploited the knowledge source aspect of Wikipedia by developing new measures of semantic associations based on Wikipedia hyperlink structure, informative-content of articles and combinations of both elements. It was found that Wikipedia can be effectively used for computing noun-noun similarity. It was also found that a model based on hybrid combinations of Wikipedia structure and informative-content based features performs better than those based on individual features. It was also found that the structure based measures outperformed the informative content based measures on both semantic similarity and semantic relatedness computation tasks. The second contribution of the research work in the thesis is that it effectively exploited the corpus aspect of Wikipedia by developing a new measure of semantic association based on asymmetric word associations. The thesis introduced the concept of asymmetric associations based measure using the idea of directional context inspired by the free word association task. The underlying assumption was that the association strength can change with the changing context. It was found that the asymmetric association based measure performed better than the symmetric measures on semantic association computation, relatedness based word choice and causality detection tasks. However, asymmetric-associations based measures have no advantage for synonymy-based word choice tasks. It was also found that Wikipedia is not a good knowledge source for capturing verb-relations due to its focus on encyclopedic concepts specially nouns. It is hoped that future research will build on the experiments and discussions presented in this thesis to explore new avenues using Wikipedia for finding deeper and semantically more meaningful associations in a wide range of application areas based on humans' estimates of word associations

Beyond treatment effect estimation; new developments in policy evaluation

To what extent are hiring incentives targeting a specific group of vulnerable unemployed people more effective with respect to generalized incentives without a definite target? Do targeted policies have negative side effects on the labor market that are too important to accept them? Which are the channels guaranteeing a long-lasting effect of European Structural Funds? Even though there is vast literature on hiring subsidies and European Structural Funds, these questions remained unanswered. To answer them, it is necessary to go beyond the simple treatment effect estimation. We did it following two different paths. To answer the first two questions we compared the impact of two similar hiring policies, one oriented towards a target group of long-term unemployed people and one generalized, implemented on the Italian labor market. We considered administrative data on job contracts and workers, and applied counterfactual analysis methods. The results show that only the targeted policy has a positive and significant impact, while the effects of the generalized policy on the vulnerable group are negligible. Moreover, we did not detect any indirect negative side effect of the targeted policy on the local labor market. To answer the third question we introduced a new methodology, namely the Mediation Analysis Synthetic Control (MASC) and applied it to investigate on the impact of European Structural Funds reduction in Abruzzi region. MASC is a generalization of the Synthetic Control Method (SCM) that allows decomposing the total effect of the intervention into its indirect effects and its direct effect when data on only one treated and few control units are available. The results show that the negative impact of European Structural Funds reduction on economic growth is mainly driven by the indirect effect passing through employment reduction. Instead, only a small portion of the negative impact is due to the indirect effect passing through investments reduction

Short message service normalization for communication with a health information system

Philosophiae Doctor - PhDShort Message Service (SMS) is one of the most popularly used services for communication between mobile phone users. In recent times it has also been proposed as a means for information access. However, there are several challenges to be overcome in order to process an SMS, especially when it is used as a query in an information retrieval system.SMS users often tend deliberately to use compacted and grammatically incorrect writing that makes the message difficult to process with conventional information retrieval systems. To overcome this, a pre-processing step known as normalization is required. In this thesis an investigation of SMS normalization algorithms is carried out. To this end,studies have been conducted into the design of algorithms for translating and normalizing SMS text. Character-based, unsupervised and rule-based techniques are presented. An investigation was also undertaken into the design and development of a system for information access via SMS. A specific system was designed to access information related to a Frequently Asked Questions (FAQ) database in healthcare, using a case study. This study secures SMS communication, especially for healthcare information systems. The proposed technique is to encipher the messages using the secure shell (SSH) protocol

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Lightweight and static verification of UML executable models

Executable models play a key role in many development methods (such as MDD and MDA) by facilitating the immediate simulation/implementation of the software system under development. This is possible because executable models include a fine-grained specification of the system behaviour using an action language. Executable models are not a new concept but are now experiencing a comeback. As a relevant example, the OMG has recently published the first version of the “Foundational Subset for Executable UML Models” (fUML) standard, an executable subset of the UML that can be used to define, in an operational style, the structural and behavioural semantics of systems. The OMG has also published a beta version of the “Action Language for fUML” (Alf) standard, a concrete syntax conforming to the fUML abstract syntax, that provides the constructs and textual notation to specify the fine-grained behaviour of systems. The OMG support to executable models is substantially raising the interest of software companies for this topic. Given the increasing importance of executable models and the impact of their correctness on the final quality of software systems derived from them, the existence of methods to verify the correctness of executable models is becoming crucial. Otherwise, the quality of the executable models (and in turn the quality of the final system generated from them) will be compromised. Despite the number of research works targetting the verification of software models, their computational cost and poor feedback makes them difficult to integrate in current software development processes. Therefore, there is the need for efficient and useful methods to check the correctness of executable models and tools integrated to the modelling tools used by designers. In this thesis we propose a verification framework to help the designers to improve the quality of their executable models. Our framework is composed of a set of lightweight static methods, i.e. methods that do not require to execute the model in order to check the desired property. These methods are able to check several properties over the behavioural part of an executable model (for instance, over the set of operations that compose a behavioural executable model) such as syntactic correctness (i.e. all the operations in the behavioural model conform to the syntax of the language in which it is described), non-redundancy (i.e. there is no another operation with exactly the same behaviour), executability (i.e. after the execution of an operation, the reached system state is -in case of strong executability- or may be -in case of weak executability- consistent with the structural model and its integrity constraints) and completeness (i.e. all possible changes on the system state can be performed through the execution of the operations defined in the executable model). For incorrect models, the methods that compose our verification framework return a meaningful feedback that helps repairing the detected inconsistencies

OSCAR. A Noise Injection Framework for Testing Concurrent Software

“Moore’s Law” is a well-known observable phenomenon in computer science that describes a visible yearly pattern in processor’s die increase. Even though it has held true for the last 57 years, thermal limitations on how much a processor’s core frequencies can be increased, have led to physical limitations to their performance scaling. The industry has since then shifted towards multicore architectures, which offer much better and scalable performance, while in turn forcing programmers to adopt the concurrent programming paradigm when designing new software, if they wish to make use of this added performance. The use of this paradigm comes with the unfortunate downside of the sudden appearance of a plethora of additional errors in their programs, stemming directly from their (poor) use of concurrency techniques. Furthermore, these concurrent programs themselves are notoriously hard to design and to verify their correctness, with researchers continuously developing new, more effective and effi- cient methods of doing so. Noise injection, the theme of this dissertation, is one such method. It relies on the “probe effect” — the observable shift in the behaviour of concurrent programs upon the introduction of noise into their routines. The abandonment of ConTest, a popular proprietary and closed-source noise injection framework, for testing concurrent software written using the Java programming language, has left a void in the availability of noise injection frameworks for this programming language. To mitigate this void, this dissertation proposes OSCAR — a novel open-source noise injection framework for the Java programming language, relying on static bytecode instrumentation for injecting noise. OSCAR will provide a free and well-documented noise injection tool for research, pedagogical and industry usage. Additionally, we propose a novel taxonomy for categorizing new and existing noise injection heuristics, together with a new method for generating and analysing concurrent software traces, based on string comparison metrics. After noising programs from the IBM Concurrent Benchmark with different heuristics, we observed that OSCAR is highly effective in increasing the coverage of the interleaving space, and that the different heuristics provide diverse trade-offs on the cost and benefit (time/coverage) of the noise injection process.Resumo A “Lei de Moore” é um fenómeno, bem conhecido na área das ciências da computação, que descreve um padrão evidente no aumento anual da densidade de transístores num processador. Mesmo mantendo-se válido nos últimos 57 anos, o aumento do desempenho dos processadores continua garrotado pelas limitações térmicas inerentes `a subida da sua frequência de funciona- mento. Desde então, a industria transitou para arquiteturas multi núcleo, com significativamente melhor e mais escalável desempenho, mas obrigando os programadores a adotar o paradigma de programação concorrente ao desenhar os seus novos programas, para poderem aproveitar o desempenho adicional que advém do seu uso. O uso deste paradigma, no entanto, traz consigo, por consequência, a introdução de uma panóplia de novos erros nos programas, decorrentes diretamente da utilização (inadequada) de técnicas de programação concorrente. Adicionalmente, estes programas concorrentes são conhecidos por serem consideravelmente mais difíceis de desenhar e de validar, quanto ao seu correto funcionamento, incentivando investi- gadores ao desenvolvimento de novos métodos mais eficientes e eficazes de o fazerem. A injeção de ruído, o tema principal desta dissertação, é um destes métodos. Esta baseia-se no “efeito sonda” (do inglês “probe effect”) — caracterizado por uma mudança de comportamento observável em programas concorrentes, ao terem ruído introduzido nas suas rotinas. Com o abandono do Con- Test, uma framework popular, proprietária e de código fechado, de análise dinâmica de programas concorrentes através de injecção de ruído, escritos com recurso `a linguagem de programação Java, viu-se surgir um vazio na oferta de framework de injeção de ruído, para esta mesma linguagem. Para mitigar este vazio, esta dissertação propõe o OSCAR — uma nova framework de injeção de ruído, de código-aberto, para a linguagem de programação Java, que utiliza manipulação estática de bytecode para realizar a introdução de ruído. O OSCAR pretende oferecer uma ferramenta livre e bem documentada de injeção de ruído para fins de investigação, pedagógicos ou até para a indústria. Adicionalmente, a dissertação propõe uma nova taxonomia para categorizar os dife- rentes tipos de heurísticas de injecção de ruídos novos e existentes, juntamente com um método para gerar e analisar traces de programas concorrentes, com base em métricas de comparação de strings. Após inserir ruído em programas do IBM Concurrent Benchmark, com diversas heurísticas, ob- servámos que o OSCAR consegue aumentar significativamente a dimensão da cobertura do espaço de estados de programas concorrentes. Adicionalmente, verificou-se que diferentes heurísticas produzem um leque variado de prós e contras, especialmente em termos de eficácia versus eficiência