5,664 research outputs found

    Exploring the Formation of a Healthcare Information Infrastructure: Hierarchy or Meshwork?

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    The digitalization of economic and social activity has brought information infrastructures (IIs) to the forefront of research. This paper studies II formation processes and their outcomes; namely, II architecture and distribution of control rights. We conduct an in-depth exploratory case study of an electronic prescription II and report on two formation processes: stratification and meshworking. The stratification process in our case study involved classifying the IIs’ diverse socio-technical components into homogeneous groups and consolidating them into a coherent hierarchical structure that standardized the components’ behavior. The outcome of this stratification was a dual and hierarchical architecture and a fairly centralized locus of control. The meshworking process, by contrast, assembled heterogeneous components without homogenizing them; the components were distributed in a way that enabled them to self-organize. The outcome of this meshworking process was a modular architecture that decoupled the central nodes from the users’ installed base and a more decentralized structure. Consequently, the final II architecture was a hybrid offering both centralized control and autonomy of the parts. Our research further illustrates how this architecture then influenced the project’s complexity and the actors’ position in the sector. We build our contribution on extant II research

    Advanced document data extraction techniques to improve supply chain performance

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    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

    Collaborative Chinese Text Recognition with Personalized Federated Learning

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    In Chinese text recognition, to compensate for the insufficient local data and improve the performance of local few-shot character recognition, it is often necessary for one organization to collect a large amount of data from similar organizations. However, due to the natural presence of private information in text data, such as addresses and phone numbers, different organizations are unwilling to share private data. Therefore, it becomes increasingly important to design a privacy-preserving collaborative training framework for the Chinese text recognition task. In this paper, we introduce personalized federated learning (pFL) into the Chinese text recognition task and propose the pFedCR algorithm, which significantly improves the model performance of each client (organization) without sharing private data. Specifically, pFedCR comprises two stages: multiple rounds of global model training stage and the the local personalization stage. During stage 1, an attention mechanism is incorporated into the CRNN model to adapt to various client data distributions. Leveraging inherent character data characteristics, a balanced dataset is created on the server to mitigate character imbalance. In the personalization phase, the global model is fine-tuned for one epoch to create a local model. Parameter averaging between local and global models combines personalized and global feature extraction capabilities. Finally, we fine-tune only the attention layers to enhance its focus on local personalized features. The experimental results on three real-world industrial scenario datasets show that the pFedCR algorithm can improve the performance of local personalized models by about 20\% while also improving their generalization performance on other client data domains. Compared to other state-of-the-art personalized federated learning methods, pFedCR improves performance by 6\% ∌\sim 8\%

    The impact of valuation rules for intangible assets in Japanese and German accounts of listed companies : [Version April 2003]

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    Intangible assets as goodwill, licenses, research and development or customer relations become in high technology and service orientated economies more and more important. But comparing the book values of listed companies and their market capitalization the financial reports seems to fail the information needs of market participants regarding the estimate of the proper firm value. Moreover, with the introduction of Anglo-American accounting systems in Europe and Asia we can observe even in the accounts of companies sited in the same jurisdiction diverging accounting practices for intangible assets caused by different accounting standards. To assess the relevance of intangible assets in Japanese and German accounts of listed companies we therefore measure certain balance sheet and profit and loss relations according to goodwill and self-developed software. We compare and analyze valuation rules for goodwill and software costs according to German GAAP, Japanese GAAP, US GAAP and IAS to determine the possible impact of diverging rules in the comparability of the accounts. Our results show that the comparability of the accounts is impaired because of different accounting practices. The recognition and valuation of goodwill and self-developed software varies significantly according to the accounting regime applied. However, for the recognition of self-developed software, the effect on the average impact on asset coefficients or profit is not that high. Moreover, an industry bias can only be found for the financial industry. In contrast, for goodwill accounting we found major differences especially between German and Japanese Blue Chips. The introduction of the new goodwill impairment only approach and the prohibition of the pooling method may have a major impact especially for Japanese companies’ accounts

    WSU Research News, Fall 2006

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    A twenty page newsletter of the WSU Research News. The WSU Research News was published monthly beginning in June of 1968 and issued by the Office of Research Development. This newsletter was created to provide information to the WSU faculty about the availability of outside funds for research and educational programs, new developments that may affect availability of funds, and general information on research and educational activities at Wright State University.https://corescholar.libraries.wright.edu/wsu_research_news/1192/thumbnail.jp
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