39,921 research outputs found

    A methodology for analysing and evaluating narratives in annual reports: a comprehensive descriptive profile and metrics for disclosure quality attributes

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    There is a consensus that the business reporting model needs to expand to serve the changing information needs of the market and provide the information required for enhanced corporate transparency and accountability. Worldwide, regulators view narrative disclosures as the key to achieving the desired step-change in the quality of corporate reporting. In recent years, accounting researchers have increasingly focused their efforts on investigating disclosure and it is now recognised that there is an urgent need to develop disclosure metrics to facilitate research into voluntary disclosure and quality [Core, J. E. (2001). A review of the empirical disclosure literature. Journal of Accounting and Economics, 31(3), 441–456]. This paper responds to this call and contributes in two principal ways. First, the paper introduces to the academic literature a comprehensive four-dimensional framework for the holistic content analysis of accounting narratives and presents a computer-assisted methodology for implementing this framework. This procedure provides a rich descriptive profile of a company's narrative disclosures based on the coding of topic and three type attributes. Second, the paper explores the complex concept of quality, and the problematic nature of quality measurement. It makes a preliminary attempt to identify some of the attributes of quality (such as relative amount of disclosure and topic spread), suggests observable proxies for these and offers a tentative summary measure of disclosure quality

    Readability, Contracts of Recurring Use, nd the Problem of Ex Post Judicial Governance of Health Insurance Policies

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    While the rhetoric surrounding the passage of the Patient Protection and Affordable Care Act focused on core issues such as cost, quality, and access to care, the dialog rarely acknowledged a key problem-the fact that most Americans do not understand their health insurance. Simply put, consumers do not fully grasp their health insurance coverage because the jargon found in many health insurance contracts is impenetrable to most Americans. This is disconcerting because consumer-oriented information is central to our increasingly consumer-directed health care system. Consumers are expected to make cost-effective choices among the array of health insurance plans that may be available to them, utilize health care services in a cost-effective manner, navigate provider networks, minimize their out-of-pocket expenses, and effectively appeal denials of coverage. Furthermore, unlike other types of insurance agreements, health insurance policies are contracts of recurring use. That is, health insurance policies are routinely and repeatedly invoked by consumers to finance their health care. Yet, such contracts are written at a level that is beyond the reading skills of most Americans. As such, insureds not only have difficultly understanding the details of their coverage, they do not fully comprehend the benefits and rights afforded by the policy. Consequently, the traditional approach of ex post judicial governance of insurance agreements (as adhesion contracts) by interpreting ambiguities in favor of insureds provides inadequate protection for health insurance consumers. If consumers do not understand their coverage rights and benefits, they cannot reasonably be expected to know when those benefits have been wrongly denied. The better, ex ante solution is to make health insurance contracts readable in the first instance by requiring that health insurance contracts meet an eighth grade readability standard as a condition of state approval

    XML Schema-based Minification for Communication of Security Information and Event Management (SIEM) Systems in Cloud Environments

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    XML-based communication governs most of today's systems communication, due to its capability of representing complex structural and hierarchical data. However, XML document structure is considered a huge and bulky data that can be reduced to minimize bandwidth usage, transmission time, and maximize performance. This contributes to a more efficient and utilized resource usage. In cloud environments, this affects the amount of money the consumer pays. Several techniques are used to achieve this goal. This paper discusses these techniques and proposes a new XML Schema-based Minification technique. The proposed technique works on XML Structure reduction using minification. The proposed technique provides a separation between the meaningful names and the underlying minified names, which enhances software/code readability. This technique is applied to Intrusion Detection Message Exchange Format (IDMEF) messages, as part of Security Information and Event Management (SIEM) system communication hosted on Microsoft Azure Cloud. Test results show message size reduction ranging from 8.15% to 50.34% in the raw message, without using time-consuming compression techniques. Adding GZip compression to the proposed technique produces 66.1% shorter message size compared to original XML messages.Comment: XML, JSON, Minification, XML Schema, Cloud, Log, Communication, Compression, XMill, GZip, Code Generation, Code Readability, 9 pages, 12 figures, 5 tables, Journal Articl

    Safurai 001: New Qualitative Approach for Code LLM Evaluation

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    This paper presents Safurai-001, a new Large Language Model (LLM) with significant potential in the domain of coding assistance. Driven by recent advancements in coding LLMs, Safurai-001 competes in performance with the latest models like WizardCoder [Xu et al., 2023], PanguCoder [Shen et al., 2023] and Phi-1 [Gunasekar et al., 2023] but aims to deliver a more conversational interaction. By capitalizing on the progress in data engineering (including latest techniques of data transformation and prompt engineering) and instruction tuning, this new model promises to stand toe-to-toe with recent closed and open source developments. Recognizing the need for an efficacious evaluation metric for coding LLMs, this paper also introduces GPT4-based MultiParameters, an evaluation benchmark that harnesses varied parameters to present a comprehensive insight into the models functioning and performance. Our assessment shows that Safurai-001 can outperform GPT-3.5 by 1.58% and WizardCoder by 18.78% in the Code Readability parameter and more.Comment: 22 pages, 1 figure, 3 table
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