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Purpose: The aim of the article was to analyse the correlation between financial liquidity and the competitiveness of enterprises from defense industry enterprises. Design/Methodology/Approach: The solutions were developed on the basis of literature analysis, case study, document analysis. As part of the case study were used diagnostic and prognostic sheets, as well as the analysis of source documents. The analysis of financial liquidity was performed using the induction method, which includes examination of details, generalization in the form of conclusions, synthetic evaluation and diagnosis. The time scope of the research covered the period 2015-2018. Four defense companies in the world were selected for the purpose of the research. Findings: The analysis of four enterprises from the defense industry shows connection between the financial situation (measured by liquidity ratios) and the competitiveness of the surveyed companies. The most stable company, one of the leading defense in the world, is Northrop Grumman. In turn, United Aircraft is characterized by a large dispersion of results and negative values of cash flows from operational activities. The article is also an overview of competitiveness, showing the diversity in managing the resources of an economic unit, and indicates practical guidelines for the use of methods and interpretation of the financial liquidity ratios of enterprises. Practical Implications: The results of the research can be used by company managers, both from the defense industry and others, to create mechanisms that increase competitiveness. Originality/Value: The study is a new contribution to research into the competitiveness of defense companies. The aspects of the competitiveness of defense enterprises on the basis of liquidity ratios remained beyond the area of scientific interest., peer-reviewed

Cross-Language Multi-document summarization (CLMDS) process produces a summary generated from multiple documents in which the summary language is different from the source document language. The CLMDS model allows the user to provide query in a particular language (e.g., Tamil) and generates a summary in the same language from different language source documents. The proposed model enables the user to provide a query in Tamil language, generate a summary from multiple English documents, and finally translate the summary into Tamil language. The proposed model makes use of naïve Bayes classifier (NBC) model for the CLMDS. An extensive set of experimentation analysis was performed and the results are investigated under distinct aspects. The resultant experimental values ensured the supremacy of the presented CLMDS model.

This review describes the state of the art on the role of various imaging modalities in pre-operative preparation to transcatheter implantation of biological aortic valve prosthesis, based on the literature data published from 2012 to 2020. One of the key source documents is the consensus of the European Society of Cardiovascular Radiology (ESCR) from 2019, providing the standardized protocols for scanning, measurements and description of the abnormalities identified. The final decision on the procedure of transcatheter aortic valve implantation should be based on the combination of all available diagnostic methods, clinical data and results of imaging by different modalities. Given the most comprehensive information content, the main diagnostic method is computed tomography. The radiologist and the multidisciplinary team should form a single entity, combining clinical and technical knowledge, discussing all the images and conclusions with other team members.

Plagiarism refers to the use of other people’s ideas and information without acknowledging the source. In this research, anti-plagiarism software was designed especially for the university and its campuses to identify plagiarized text in students’ written assignments and laboratory reports. The proposed framework collected original documents to identify plagiarized text using natural language processing. Our research proposes a method to detect plagiarism by applying the core concept of text, which is semantic associations of words and their syntactic composition. Information on the browser was obtained through Request application programming interface by name Url.AbsoluteUri, and it is stored in a centralized Microsoft database Server. A total of 55,001 data samples were collected from 2015 to 2019. Furthermore, we assimilated data from a university website, specifically from the psau.edu.sa network, and arranged the data into students’ categories. Furthermore, we extracted words from source documents and student documents using the WordNet library. On a benchmark dataset consisting of 785 plagiarized text and 4,716 original text data, a significant accuracy of 90.2% was achieved. Therefore, the proposed framework demonstrated better performance than the other available tools. Many students mentioned that working on assignments using the framework was suitable because they were able to work on the assignments in harmony, as per their timeframe and from different network locations. The framework also recommends procedures that can be used to avoid plagiarism.

The issues of calculating losses caused by criminal acts of an economic nature are traditionally debatable in Russian law. This problem is especially acute in industries with specific conditions of financial and economic activity, for example, in the agricultural sector. The paper contains the author’s interpretation of the term "losses" in accordance with the specifics of agricultural production. The proposed method for calculating losses is differentiated according to the objects of criminal offense and is based on the use of primary accounting documents, accounting registers and specialized reporting forms, which have not been previously studied and described in the scientific literature. The paper proposes methods for calculating losses caused by economic crimes in the agricultural sector in conditions of neglect or lack of accounting. The author’s position regarding the inclusion of the amounts of lost profits in the assessment of damage is presented and possible conditions for its recognition are described.

Municipalities voluntarily carry out two main tasks: in the field of sport and recrea-tion and culture. In most communities there are sports clubs, including folk clubs in villages. Community expenditure on physical culture accounts for several percent of the budgeted expendi-ture. The purpose of the article is to answer the question as to whether, despite relatively small expenditure on sport, in relation to other titles, municipalities control the effectiveness of funds spent on this voluntary task. To this end, an analysis of legal acts regulating the possible forms of spending funds on sport in municipalities was conducted. The author also used the knowledge acquired in cooperation with municipalities. Mainly two research methods were used: research of source documents and observation. The study arrives at the following conclusion: municipalities do not assess the efficiency of spending on sport. Such conduct is contrary to the principles of spending public funds. Each expenditure should be purposeful, economical, and effective. The mechanisms of expenditure efficiency assessment proposed by the author are adequate to the organizational solutions adopted in communities for the implementation of tasks in the field of sport and thus forms of financing them. To ensure the effectiveness of the proposed mechanisms, they should be included in the management control system of the community.

PurposeThe purpose of the study is to assess performance of local level planning policies that required new buildings to avoid a specified and rising proportion of projected greenhouse gases (GHGs) from their use; it is calculated based on the approved design and plans for the specific development and through the installation and operation of low and zero-carbon generating technologies (LZCGTs).Design/methodology/approachData were extracted from a random sample of 911 new builds from 403 planning applications and related documents, across five Scottish local planning authorities (LPAs) who adopted GHG reduction policies. The data included GHG reduction, LZCGT installation and performance, use of plan designs to meet GHG reductions and exemptions from the GHG policies. Descriptive statistics using SPSS software, complimented by qualitative responses from questionnaires, helped to explain observed performance.FindingsThe policies performed poorly, at the level of delivering low-hanging fruits, with significant room for improvement. Design-led opportunities in the GHG policies were not actively pursued; most LZCGT installation was exempted from GHG policies and the policies were poor in targeting the relationship between building unit size, GHG emission and reductions.Research limitations/implicationsThe source documents, where the data came from, had varying quality and completeness and some LPAs are over-represented in the data. The study applied limited criteria to evaluate policy performance.Practical implicationsAreas for policymakers to further focus on when exploring how to enhance role and performance of LZCGT are highlighted, including practical suggestions.Originality/valueOne of the few studies assessing policy performance and distilling lessons, from early adopters of GHG policies at local level planning, offer performance benchmarks and raise points of concern for policymakers.

Niniejszy artykuł porusza tematykę funkcjonowania rynku obligacji komunalnych w Polsce. Analiza obejmuje lata 2012-2018. W opracowaniu zwrócono uwagę również na rolę Catalyst w rozwoju dłużnych papierów wartościowych. Materiałem badawczym w niniejszym artykule są: analiza literatury i aktów prawnych, metoda obserwacji, analiza dokumentów źródłowych oraz metoda dedukcji. Rezultatem rozważań jest określenie obszarów, na które należy zwrócić uwagę w celu wzrostu zainteresowania rynkiem obligacji komunalnych. Przeprowadzona analiza wykazała, iż jednostki samorządu terytorialnego, dokonując emisji długu, przyczyniły się w niewielkim stopniu do rozwoju tego rynku. Również powstanie platformy Catalyst spowodowało nieznaczny wzrost zainteresowania emisją tego rodzaju papierów dłużnych, wyższy udział w finansowaniu zadłużenia w analizowanym okresie miały kredyty i pożyczki

The architecture of the text-reuse detection system consists of three main modules, i.e., source retrieval, text analysis, and knowledge-based postprocessing. Each module plays an important role in the accuracy rate of the detection outputs. Therefore, this research focuses on developing the source retrieval system in cases where the source documents have been obfuscated in different levels. Two steps of term weighting were applied to get such documents. The first was the local-word weighting, which has been applied to the test or reused documents to select query per text segments. The tf-idf term weighting was applied for indexing all documents in the corpus and as the basis for computing cosine similarity between the queries per segment and the documents in the corpus. A two-step filtering technique was applied to get the source document candidates. Using artificial cases of text reuse testing, the system achieves the same rates of precision and recall that are 0.967, while the recall rate for the simulated cases of reused text is 0.66.

With the continuous development of deep learning, pre-trained models have achieved sound effects in the field of natural language processing. However, text summarization research is far from what people want, especially in abstractive summarization. A high-quality summarization system needs to focus on the topic content of the document and the similarity between the summary and the source document. In this paper, we propose a topic information fusion and semantic relevance for text summarization based on Fine-tuning BERT(TIF-SR). Primarily, considering the critical role of topic information in summary generation, we extract topic keywords and fusion them with source documents as part of the input. Secondly, make the summary closer to the source document by calculating the semantic similarity between the generated summary and the source document, the quality of the abstract is improved. The experimental data indicate that the ROUGE index and readability have improved in this model, so these shreds of evidence suggest that the method proposed by our model is sufficient.

Disparities of socio-economic development, barriers and limitations and social expectations are the circumstances for new look into city, its model and city space management. For this purpose one makes review of space in various ranges and indicates the changes to be done for improving the conditions of territory functionality. A part of a space stays untouched, not to interfere with its previous development. However, a part of territory needs deploying of revitalization process. Particularly it relates to post-industrial, post-military, post-railway, outlying, bad managed or not managed areas. The consequences of city development are changes in its functional arrangement and pushing out some functions to outlying areas or outside of a city. The goal of this article is to research the renewed problem of post-military areas management. The areas have quality diversified localizations and their transfer is followed in diversified technical and functional conditions. The analysis of revitalized post-military areas for some examples. There have been used the following research methods: literature and source documents analysis, deduction and case study.

Background There is an unmet need for review methods to support priority-setting, policy-making and strategic planning when a wide variety of interventions from differing disciplines may have the potential to impact a health outcome of interest. This article describes a Modular Literature Review, a novel systematic search and review method that employs systematic search strategies together with a hierarchy-based appraisal and synthesis of the resulting evidence. Methods We designed the Modular Review to examine the effects of 43 interventions on a health problem of global significance. Using the PICOS (Population, Intervention, Comparison, Outcome, Study design) framework, we developed a single four-module search template in which population, comparison and outcome modules were the same for each search and the intervention module was different for each of the 43 interventions. A series of literature searches were performed in five databases, followed by screening, extraction and analysis of data. “ES documents”, source documents for effect size (ES) estimates, were systematically identified based on a hierarchy of evidence. The evidence was categorised according to the likely effect on the outcome and presented in a standardised format with quantitative effect estimates, meta-analyses and narrative reporting. We compared the Modular Review to other review methods in health research for its strengths and limitations. Results The Modular Review method was used to review the impact of 46 antenatal interventions on four specified birth outcomes within 12 months. A total of 61,279 records were found; 35,244 were screened by title-abstract. Six thousand two hundred seventy-two full articles were reviewed against the inclusion criteria resulting in 365 eligible articles. Conclusions The Modular Review preserves principles that have traditionally been important to systematic reviews but can address multiple research questions simultaneously. The result is an accessible, reliable answer to the question of “what works?”. Thus, it is a well-suited literature review method to support prioritisation, decisions and planning to implement an agenda for health improvement.

The World Health Organization (WHO) has collected information on policies on sexual, reproductive, maternal, newborn, child and adolescent health (SRMNCAH) over many years. Creating a global survey that works for every country context is a well-recognized challenge. A comprehensive SRMNCAH policy survey was conducted by WHO from August 2018 through May 2019. WHO regional and country offices coordinated with Ministries of Health and/or national institutions who completed the questionnaire. The survey was completed by 150 of 194 WHO Member States using an online platform that allowed for submission of national source documents. A validation of the responses for selected survey questions against content of the national source documents was conducted for 101 countries (67%) for the first time in the administration of the survey. Data validation draws attention to survey questions that may have been misunderstood or where there was a lot of missing data, but varying methods for validating survey responses against source documents and separate analysis of laws from policies and guidelines may have hindered the overall conclusions of this process. The SRMNCAH policy survey both provided a platform for countries to track their progress in adopting WHO recommendations in national SRMNCAH-related legislation, policies, guidelines and strategies and was used to create a global database and searchable document repository. The outputs of the SRMNCAH policy survey are resources whose importance will be enriched through policy dialogues and wide utilization. Lessons learned from the methodology used for this survey can help to improve future updates and inform similar efforts.

The paper outlines procedures necessary for development of advanced teaching materials in a large scale and describes a tool that can help the individual authors to improve the efficiency of the process through a semi-automated conversion of source documents to a form suitable for presentation on web pages. The tool (in its prototype version) has been already tested within a nationwide project.

Extraction of categorised named entities from text is a complex task given the availability of a variety of Named Entity Recognition (NER) models and the unstructured information encoded in different source document formats. Processing the documents to extract text, identifying suitable NER models for a task, and obtaining statistical information is important in data analysis to make informed decisions. This paper presents TNNT, a toolkit that automates the extraction of categorised named entities from unstructured information encoded in source documents, using diverse state-of-the-art Natural Language Processing (NLP) tools and NER models. TNNT integrates 21 different NER models as part of a Knowledge Graph Construction Pipeline (KGCP) that takes a document set as input and processes it based on the defined settings, applying the selected blocks of NER models to output the results. The toolkit generates all results with an integrated summary of the extracted entities, enabling enhanced data analysis to support the KGCP, and also, to aid further NLP tasks., This demo paper will be submitted at K-Cap 2021

A Development Safety Update Report (DSUR) is a comprehensive review of safety information collected during an annual reporting period for an investigational drug. The creation of a DSUR is a resource-intensive process, requiring effective communication among, and collaboration with, various functional stakeholders. To increase the efficiency and cost-effectiveness of generating a DSUR with the use of an electronic authoring platform. This computerized platform auto-populates the DSUR template using available source data. Requirements for the DSUR authoring tool were developed, and available data sources were mapped to respective sections of a company DSUR template. The DSUR authoring tool, mined, scanned, and extracted the relevant data from multiple-input source documents using Natural Language Processing logic. The data were then auto-populated into the template, as required by regulatory guidance ICH-E2F, with different text options based on questions and answers. The time- and cost-savings gained through automation were analyzed for DSURs authored during 2017–2019. The DSUR contains 31 sections (20 main sections and 11 sub-sections), of which 9 were fully automated, 12 were partially automated, and 10 were not automated in this release of the automation tool. Use of the DSUR automation tool resulted in a time-savings of approximately 25% per report, corresponding to a cost saving of US$4550 per report. Deployment of the automation tool reduced the time and cost required to manually produce DSURs, as well as improved the quality, compliance, and consistency of company-prepared DSURs. This tool enhanced the potential of the organization to generate larger volumes of DSURs in a timely manner, without requiring additional resources or compromising quality. This or a similar authoring automation tool could be employed in the future to generate other regulatory submission documents, which also require various source documents and cross-functional contributions.

Health management information systems (HMIS) are a crucial source of timely health statistics and have the potential to improve reporting in low-income countries. However, concerns about data quality have hampered their widespread adoption in research and policy decisions. This article presents results from a data verification study undertaken to gain insights into the quality of HMIS data in Ethiopia. We also provide recommendations for working with HMIS data for research and policy translation. We linked the HMIS to the 2016 Emergency Obstetric and Newborn Care Assessment, a national census of all health facilities that provided maternal and newborn health services in Ethiopia. We compared the number of visits for deliveries and caesarean sections (C-sections) reported in the HMIS in 2015 (January–December) to those found in source documents (paper-based labour and delivery and operating theatre registers) in 2425 facilities across Ethiopia. We found that two-thirds of facilities had ‘good’ HMIS reporting for deliveries (defined as reporting within 10% of source documents) and half had ‘very good’ reporting (within 5% of source documents). Results were similar for reporting on C-section deliveries. We found that good reporting was more common in urban areas (OR: 1.30, 95% CI 1.06 to 1.59), public facilities (OR: 2.95, 95% CI 1.38 to 6.29) and in hospitals compared with health centres (OR: 1.71, 95% CI 1.13 to 2.61). Facilities in the Somali and Afar regions had the lowest odds of good reporting compared with Addis Ababa and were more likely to over-report deliveries in the HMIS. Further work remains to address remaining discrepancies in the Ethiopian HMIS. Nonetheless, our findings corroborate previous data verification exercises in Ethiopia and support greater use and uptake of HMIS data for research and policy decisions (particularly, greater use of HMIS data elements (eg, absolute number of services provided each month) rather than coverage indicators). Increased use of these data, combined with feedback mechanisms, is necessary to maintain data quality.

In abstractive single-document summarization task, generated summaries always suffer from fabricated and less informative content. An intuitive way to alleviate this problem is to merge external semantic knowledge into the model framework. In this paper, we incorporate explicit graphs based on semantic knowledge, including term frequency, discourse information, and entities with their relations, into neural abstractive summarization for the problem. We propose a novel model for abstractive single-document Summarization based on Semantic Knowledge Graphs (SKGSUM), which regards sentences and entities as nodes, captures the relations between units in different textual levels, and focuses on salient content in the source documents to guide the summary generation process. To the best of our knowledge, we are the first to exploit different textual-unit levels explicit graph representations in a unified framework for the neural abstractive summarization task. Results show that our model achieves significant improvements on both XSum and CNN/Daily Mail datasets over some strong baselines. Human evaluations further indicate that our model can generate informative and coherent summaries.

Medical knowledge bases (KBs), distilled from biomedical literature and regulatory actions, are expected to provide high-quality information to facilitate clinical decision making. Entity disambiguation (also referred to as entity linking) is considered as an essential task in unlocking the wealth of such medical KBs. However, existing medical entity disambiguation methods are not adequate due to word discrepancies between the entities in the KB and the text snippets in the source documents. Recently, graph neural networks (GNNs) have proven to be very effective and provide state-of-the-art results for many real-world applications with graph-structured data. In this paper, we introduce ED-GNN based on three representative GNNs (GraphSAGE, R-GCN, and MAGNN) for medical entity disambiguation. We develop two optimization techniques to fine-tune and improve ED-GNN. First, we introduce a novel strategy to represent entities that are mentioned in text snippets as a query graph. Second, we design an effective negative sampling strategy that identifies hard negative samples to improve the model's disambiguation capability. Compared to the best performing state-of-the-art solutions, our ED-GNN offers an average improvement of 7.3% in terms of F1 score on five real-world datasets., To appear in SIGMOD 2021

This study was designed to collect data on key issues related to implementation of the digital forms of tax checks, in particular e-audit, Fiscal Services of different countries. The research is focused on three-areas: a) to explore the essence and the structure of Standard Audit File for Tax; b) to learn the international experience of using tax audit files; c) to develop recommendations on the possibility of introducing the most effective methods in national practice.Standard Audit File for Tax (SAF-T) is an electronic file, which contains accurate audit data, exported from the initial accounting system. SAF-T 1.0 consists of four sections: «Header», «Master Files», «General Ledger Entries» and «Source Documents». In version 2.0, 8 subunits were added to the «Master Files» section: «Taxonomies», «UOMT Table», «Analysis Type Table», «Transaction Type Table», «Products», «Physical Stock», «Owners» and «Assets».The experience of the implementation and components of tax audit files of the leading European countries indicate that: a) most countries use the tax audit file in accordance with the recommendations of the OSER (Portugal, Norway, Austria, Lithuania), but there are some that have substantially modified it (Singapore); b) there are different ways to provide data: from pilot projects and voluntary application, to mandatory filing by certain categories or by all taxpayers; c) mainly the fiscal authorities apply the phased implementation of tax audit files in practice (Austria, Lithuania, Norway, etc.).It is established that before introducing SAF-T into national practice, it is worth considering that the standard approach can be neither practical nor desirable because each tax service faces a variety of conditions, the legislative environment, administrative practice and the tax culture of taxpayers.

This article considers document flow in the management system of chain trade companies. Taking into account the specific character of their activities, chain trade companies constantly face the demand for unification of document processing. Volume of document flow in chain companies is significant and rather costly. At the same time it constantly requires optimization in accordance with the transformation of a company’s business processes posed by the changing market. The paper examines arrangements for organizing document flow of chain retailers, defines general components of accounting policies, classifies major documents used in the company’s operating activities. The author also identifies stages of document flow in chain retailing companies starting from the interface of interaction with suppliers up to the interface of interaction with consumers. The results of the study show that while considering document flow as an imparting business process, it is important to take into account the documentation of core and control processes of an enterprise, due to the fact that in the majority of cases documents serve as an input and output of various business processes. In its turn, performance of the entire chain trade company depends on the proper document processing.

The aim of the article is the general characterization of cyberterrorism as a new and constantly evolving form of terrorism. The phenomenon was described in the context of globalization processes and the main definitions of terrorism were cited, followed by the specific factors determining cyberterrorism. The article was based on an analysis of source documents – legislation on terrorism prevention and cyberspace protection. Definitions review was performed. The results of earlier research related to the topic of work are quoted. It has been assessed that in the future the threat of cyber-attacks will be a challenge for states and international organizations. The development of modern technologies and the creation of computerized societies is a factor that provides new areas of action for terrorists. Potential targets of attacks are varied and difficult to protect. Preventive actions should be characterized by a comprehensive approach to the problem and systemic, long-term action in the international sphere.

Because of M*A*S*H and other popular portrayals, helicopter evacuation of casualties has been closely linked to the Korean War. We sought to investigate their role in military medicine during this conflict.This study incorporated a thorough review of the original source documents dating to the Korean War that are housed in the National Archives, the Military History Institute, and other repositories.Medical evacuation helicopters entered the war late, after the United Nations forces had suffered the majority of their casualties. There were relatively few helicopters in the country, and a combination of mechanical and personnel issues kept many grounded. Technological constraints limited their efficacy. Military policy forbade rescues from the front lines, and interhospital transfers comprised a significant percentage of their missions.Helicopters did not appreciably decrease the average time from wounding to surgical care, nor did they evacuate a statistically significant number of casualties, and ultimately, they had minimal effect on military medicine. However, the war did provide helicopters the opportunity to prove themselves conceptually, leading to their widespread usage in Vietnam, in later conflicts, and ultimately in civilian health care systems.

Background Care for patients with thyroid nodules is complex and multidisciplinary, and research demonstrates variation in care. The objective was to develop clinical guidelines and quality metrics to reduce unwarranted variation and improve quality. Methods Multidisciplinary expert consensus and modified Delphi approach. Source documents were workflow algorithms from Kaiser Permanente Northern California and Cancer Care of Ontario based on the 2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer. Results A consensus-based, unified preoperative, perioperative, and postoperative workflow was developed for North American use. Twenty-one panelists achieved consensus on 16 statements about workflow-embedded process and outcomes metrics addressing safety, access, appropriateness, efficiency, effectiveness, and patient centeredness of care. Conclusion A panel of Canadian and United States experts achieved consensus on workflows and quality metric statements to help reduce unwarranted variation in care, improving overall quality of care for patients diagnosed with thyroid nodules.

Automation of pharmaceutical safety case processing represents a significant opportunity to affect the strongest cost driver for a company's overall pharmacovigilance budget. A pilot was undertaken to test the feasibility of using artificial intelligence and robotic process automation to automate processing of adverse event reports. The pilot paradigm was used to simultaneously test proposed solutions of three commercial vendors. The result confirmed the feasibility of using artificial intelligence–based technology to support extraction from adverse event source documents and evaluation of case validity. In addition, the pilot demonstrated viability of the use of safety database data fields as a surrogate for otherwise time‐consuming and costly direct annotation of source documents. Finally, the evaluation and scoring method used in the pilot was able to differentiate vendor capabilities and identify the best candidate to move into the discovery phase.

Word embeddings are increasingly being used as a tool to study word associations in specific corpora. However, it is unclear whether such embeddings reflect enduring properties of language or if they are sensitive to inconsequential variations in the source documents. We find that nearest-neighbor distances are highly sensitive to small changes in the training corpus for a variety of algorithms. For all methods, including specific documents in the training set can result in substantial variations. We show that these effects are more prominent for smaller training corpora. We recommend that users never rely on single embedding models for distance calculations, but rather average over multiple bootstrap samples, especially for small corpora.

ive summarization is useful in providing a summary or a digest of news or other web texts and enhancing users reading experience, especially when they are reading on small displays such as mobile phones. However, existing encoder-decoder summarization models have difficulty learning the latent alignment between source documents and summaries because of their vast disparity in length. In this paper, we propose a extractor-abstractor framework in which the keyword-based extractor selects a few sets of salient sentences from the input document and then the abstractor paraphrases these sets of sentences in parallel, which are more aligned to the summary, to generate the final summary. The new extractor and abstractor are pretrained from a set of “pseudo summaries” extracted by specially designed heuristics, and then further trained together in a reinforcement learning framework. The results show that the proposed model generates high-quality summaries with faster training speed and less training memory footprint, and outperforms the state-of-the-art models on CNN/Daily Mail, Webis-TLDR-17, Webis-Snippet-20, WikiHow and DUC-2002 datasets.

Feature models (FM) are a valuable resource in the analysis of software product lines (SPL). They provide a visual abstraction of the variation points in a family of related software products. FMs can be manually created by domain experts or extracted (semi-) automatically from textual documents such as product descriptions or requirements specifications. Nevertheless, there is no way to measure the accuracy of a FM with respect to the information described in the source documents. This paper proposes a method to quantify and visualize whether the elements in a FM (features and relationships) conform to the information available in a set of specification documents. Both the correctness (choice of representative elements) and completeness (no missing elements) of the FM are considered. Designers can use this feedback to fix defects in the FM or to detect incomplete or inconsistent information in the source documents.

ive text summarization aims at compressing the information of a long source document into a rephrased, condensed summary. Despite advances in modeling techniques, abstractive summarization models still suffer from several key challenges: (i) layout bias: they overfit to the style of training corpora; (ii) limited abstractiveness: they are optimized to copying n-grams from the source rather than generating novel abstractive summaries; (iii) lack of transparency: they are not interpretable. In this work, we propose a framework based on document-level structure induction for summarization to address these challenges. To this end, we propose incorporating latent and explicit dependencies across sentences in the source document into end-to-end single-document summarization models. Our framework complements standard encoder-decoder summarization models by augmenting them with rich structure-aware document representations based on implicitly learned (latent) structures and externally-derived linguistic (explicit) structures. We show that our summarization framework, trained on the CNN/DM dataset, improves the coverage of content in the source documents, generates more abstractive summaries by generating more novel n-grams, and incorporates interpretable sentence-level structures, while performing on par with standard baselines.

In our modern society, almost all events, processes, and decisions in a corporation are documented by internal written communication, legal filings, or business and financial news. The valuable knowledge in such collections is not directly accessible by computers as they mostly consist of unstructured text. This chapter provides an overview of corpora commonly used in research and highlights related work and state-of-the-art approaches to extract and represent financial entities and relations.The second part of this chapter considers applications based on knowledge graphs of automatically extracted facts. Traditional information retrieval systems typically require the user to have prior knowledge of the data. Suitable visualization techniques can overcome this requirement and enable users to explore large sets of documents. Furthermore, data mining techniques can be used to enrich or filter knowledge graphs. This information can augment source documents and guide exploration processes. Systems for document exploration are tailored to specific tasks, such as investigative work in audits or legal discovery, monitoring compliance, or providing information in a retrieval system to support decisions.

ive multi-document summarisation (MDS) remains a challenging task. Part of the problem is the question as to how to preserve a document’s polarity in the summary. We propose an opinion polarity attention model for MDS, which incorporates a polarity estimator based on a BERT-GRU sentiment analysis network. It captures the impact of opinions expressed in the source documents and integrates it in the attention mechanism. Experimental results using a state-of-the-art MDS approach and a common benchmark test collection demonstrate that this model has a measurable positive effect using a range of metrics.

Purpose: The purpose of this article is to assess whether having a creditworthiness assessment from more than one credit rating agency by issuers of green debt instruments affects the number of issues and the average amount issued. This objective was achieved through the process of analysis of the ratings assigned by the rating agencies. Methodology: Due to the complexity of the data, mixed research methods were used such as observation, analysis, and inference. The study used data from Refinitiv Eikon. The credit ratings for the issuers, announced on the issue date, have been analyzed. The analysis covered debt instruments issued in all currencies in the years 2010-2020. The empirical research was carried out using the observation method, the analysis of source documents, and the method of deduction. Findings: In the analyzed period only 44.22% of issuers had credit ratings. The results of the conducted research indicate that the number of green debt instruments and the average issue amount were affected by the number of ratings given to the issuer. Because the largest number of green debt instruments and the highest average issue amount was held by green debt issuers with three ratings, there is a need for an additional creditworthiness assessment. Practical implications: The conclusions of this study can be used in the process of obtaining financing from capital markets on green projects. The content of the article concerns the conclusions from the analysis of the credit ratings of the issuers of green debt instruments. Originality/Value: The theoretical and empirical literature does not provide guidance on the impact of the issuer's credit rating on the success of issuing debt instruments, in particular green ones., peer-reviewed

Text summarization is the process of generating the condensed view of the text by selecting useful and relevant information from the original source documents. It is a sub-topic of natural language Processing. Text summarization is a technique for understanding the aim of any document, to visualize large text document within short duration. Summarization provides flexibility and convenience. Business leaders, analyst, and researchers must undergo huge number of documents on a day-to-day basis to stay ahead, and an oversized portion of their time is spent just deciding what document has relevancy and what is not. By finding out important and relevant sentences and creating summaries, it is possible to quickly check whether or not a document is worth reading. In this paper, we propose to design automatic text summarizer to summarize the multiple text documents. The input to the system is the multiple sources of news articles. Important sentences from the source document are selected and arranged in the destination documents or the summarized documents. This is called as the extraction technique in automatic text summarization. Here, one document visited is being processed, and its summary is generated using extraction technique through which a weighted graph is constructed using language processing.

Background and Objectives This article examines a cervical screening incident from the 1960s and draws lessons for screening policy. Study Design and Setting Concern about harmful overtreatment of symptomless lesions prompted university gynecologist Herbert Green to study, between 1965 and 1970, a ‘special series’ of 33 women with carcinoma in situ (CIS) who were managed with only limited punch or wedge biopsy. These women were carefully followed up but not treated unless they showed evidence of progression to invasive cancer. This paper examines source documents and subsequent publications in order to ascertain lessons from this incident. Results In keeping with the 1964 Helsinki Declaration, written consent was not sought. Green published the outcomes for his patients with CIS including the ‘special series.’ A Judicial inquiry (the Cartwright Inquiry) in 1987 concluded that some women had suffered harm and some had died, but numbers and evidence were not clearly stated. Medical case review for the Inquiry identified 25 women with only punch or wedge biopsy; in 21 of these, there were reasons why no further treatment was given; two had developed cervical cancer, and none were recorded as having died. The case review found eight patients, not necessarily in the ‘special series,’ who ‘in retrospect and by 1987 standards’ might have benefited from earlier conisation or hysterectomy. Conclusion Subsequent claims relating to Green's practice have wrongly stated that as many as one hundred women or more had treatment withheld and over 30 died as a result. These claims are inaccurate.

We study annotation projection in text classification problems where source documents are published in multiple languages and may not be an exact translation of one another. In particular, we focus on the detection of unfair clauses in privacy policies and terms of service. We present the first English-German parallel asymmetric corpus for the task at hand. We study and compare several language-agnostic sentence-level projection methods. Our results indicate that a combination of word embeddings and dynamic time warping performs best.

IntroductionReliable Health Management and Information System (HMIS) data can be used with minimal cost to identify areas for improvement and to measure impact of healthcare delivery. However, variable HMIS data quality in low- and middle-income countries limits its value in monitoring, evaluation and research. We aimed to review the quality of Rwandan HMIS data for maternal and newborn health (MNH) based on consistency of HMIS reports with facility source documents.MethodsWe conducted a cross-sectional study in 76 health facilities (HFs) in four Rwandan districts. For 14 MNH data elements, we compared HMIS data to facility register data recounted by study staff for a three-month period in 2017. A HF was excluded from a specific comparison if the service was not offered, source documents were unavailable or at least one HMIS report was missing for the study period. World Health Organization guidelines on HMIS data verification were used: a verification factor (VF) was defined as the ratio of register over HMIS data. A VF1.10 indicated over- and under-reporting in HMIS, respectively.ResultsHigh proportions of HFs achieved acceptable VFs for data on the number of deliveries (98.7%;75/76), antenatal care (ANC1) new registrants (95.7%;66/69), live births (94.7%;72/76), and newborns who received first postnatal care within 24 hours (81.5%;53/65). This was slightly lower for the number of women who received iron/folic acid (78.3%;47/60) and tested for syphilis in ANC1 (67.6%;45/68) and was the lowest for the number of women with ANC1 standard visit (25.0%;17/68) and fourth standard visit (ANC4) (17.4%;12/69). The majority of HFs over-reported on ANC4 (76.8%;53/69) and ANC1 (64.7%;44/68) standard visits.ConclusionThere was variable HMIS data quality by data element, with some indicators with high quality and also consistency in reporting trends across districts. Over-reporting was observed for ANC-related data requiring more complex calculations, i.e., knowledge of gestational age, scheduling to determine ANC standard visits, as well as quality indicators in ANC. Ongoing data quality assessments and training to address gaps could help improve HMIS data quality.

Background: The article discusses the role of customer value on the electricity market in Poland. The author characterizes the power sector in Poland and describes its transformations in connec­tion with Poland’s accession to the European Union, paying attention to the important role and importance of customer value in the power sector as the basis for an appropriate formulation of the customer service policy of the power company Research purpose: The goal of the article is to present selected aspects of customer value, including its role in the power sector as the basis for creating customer service policy based on source data and the results of domestic and foreign scientific research. Methods: The research method used is the document examination method and the method of analysis and criticism of the literature. The following research techniques were used in the article: analysis of domestic and foreign literature on the subject, analysis of source documents (existing sources) – desk research, analysis of case studies described in the literature on the subject and methods of inference (deduction and induction). Conclusions: Due to the increasing freedom of decision making, caused by the adaptation of Polish electricity regulations to European Union regulations, the needs and expectations of the customer of the Polish electricity sector have gained importance. This is reflected in the desire to know what constitutes value and brings satisfaction to the client. Including the needs and expectations of cus­tomers in the company’s offer affects the level of customer service and, consequently, their loyalty.

Organisations deal with a large set of source documents, often concerning law and regulation, that they need to comply to or bring to execution. In the past, a team of legal specialists analysed these documents and wrote new documents containing informal preliminary specifications which are hard to validate. Another group of experts translates these documents to specifications for IT-systems.

International audience; The popularization of social networks and digital documents has quickly increased the multilingual information available on the Internet. However, this huge amount of data cannot be analyzed manually. This paper deals with Cross-Language Text Summarization (CLTS) that produces a summary in a different language from the source documents. We describe three compressive CLTS approaches that analyze the text in the source and target languages to compute the relevance of sentences. Our systems compress sentences at two levels: clusters of similar sentences are compressed using a multi-sentence compression (MSC) method and single sentences are compressed using a Neural Network model. The version of our approach using multi-sentence compression generated more informative French-to-English cross-lingual summaries than extractive state-of-the-art systems. Moreover, these cross-lingual summaries have a grammatical quality similar to extractive approaches.

INTRODUCTION: During systematic reviews, data abstraction is labor- and time-intensive and error-prone. Existing data abstraction systems do not track specific locations and contexts of abstracted information. To address this limitation, we developed a software application, the Data Abstraction Assistant (DAA), and surveyed early users about their experience using DAA. FEATURES OF DAA: We designed DAA to encompass three essential features: (1) a platform for indicating the source of abstracted information; (2) compatibility with a variety of data abstraction systems; and (3) user-friendliness. HOW DAA FUNCTIONS: DAA: (1) converts source documents from PDF to HTML format (to enable tracking of source of abstracted information); (2) transmits the HTML to the data abstraction system; and (3) displays the HTML in an area adjacent to the data abstraction form in the data abstraction system. The data abstractor can mark locations on the HTML that DAA associates with items on the data abstraction form. EXPERIENCES OF EARLY USERS OF DAA: When we surveyed 52 early users of DAA, 83% reported that using DAA was either very or somewhat easy; 71% are very or somewhat likely to use DAA in the future; and 87% are very or somewhat likely to recommend that others use DAA in the future. DISCUSSION: DAA, a user-friendly software for linking abstracted data with their exact source, is likely to be a very useful tool in the toolbox of systematic reviewers. DAA facilitates verification of abstracted data and provides an audit trail that is crucial for reproducible research.systematic reviews; data abstraction; software; data tracking; data exchange

A sea change in the management of diabetes is occurring with the publication of clinical trials showing unequivocal cardiovascular (CV) protection through the use of certain antihyperglycemic agents. This change is similar to the change that occurred when lipid lowering with statins was first shown to have CV benefits, an event necessitating changes in training and the proactive treatment of lipids by CV specialists. As was the case then, many CV specialists currently feel poorly equipped to address diabetes with this new information even though diabetes is common in CV practice. The purpose of this overview is to provide an updated, comprehensive, and evidence-based CV protection plan for patients with type 2 diabetes, intended specifically for cardiologists and vascular medicine specialists. We attempt to elucidate a set of "CardioDiabetes" core competencies by merging the CV-relevant elements of the Diabetes Canada 2018 guidelines within a framework of comprehensive vascular protection as supported by other CV guidelines. We review the rationale for measuring hemoglobin A1C, understanding its use for establishing a diagnosis and for monitoring treatment. We also provide a brief review of the medications most important for a CV specialist to know. We provide useful memory aids and a succinct set of reminders and tips ("ABCDEFR'S") that can serve as a comprehensive checklist in the clinic and help to motivate trainees and clinicians to consult the original guideline source documents to enrich their knowledge and improve treatment in this rapidly changing arena.

In this article we present a new semantic and syntactic-based method for external plagiarism detection. In the proposed approach, latent dirichlet allocation (LDA) and parts of speech (POS) tags are used together to detect plagiarism between the sample and a number of source documents. The basic hypothesis is that considering semantic and syntactic information between two text documents may improve the performance of the plagiarism detection task. Our method is based on two steps, naming, which is a pre-processing where we detect the topics from the sentences in documents using the LDA and convert each sentence in POS tags array; then a post processing step where the suspicious cases are verified purely on the basis of semantic rules. For two types of external plagiarism (copy and random obfuscation), we empirically compare our approach to the state-of-the-art N-gram based and stop-word N-gram based methods and observe significant improvements.

Purpose – Getting an answer to the question whether shared services centres created in Polish communes after the change of the political system are, in fact, a new solution in comparison to previously functioning teams of educational units service. Design/methodology/approach – Literature study, analysis of legal regulations, analysis of source documents and reasoning. Findings – Shared services centres gained recognition within the sphere of Polish local government activities, however, they are not used efficiently enough. Communes, in general, create shared services centres based on previously functioning teams of educational units service and focus mainly on servicing these units. Originality/value – The research outlined current nature of a common service in Polish communes. Its results may provide the basis for further analysis that seeks an answer to the question on the reasons for the lack of a wider application of shared services centres in Polish local government activities.

Historical accounts of the earliest experiments in blood transfusion celebrate work done in France and England in 1667 to 1668. Less attention has been given to pioneering experiments conducted at the same period in Italy. We review records of the first blood transfusion experiments conducted in 17th century Italy. Using original source documents, we provide details of early experiments focusing on the identity of early researchers, their instruments, and techniques. Accounts of the period describe animal-to-animal, animal-to-human, and human-to-human transfusions. We highlight how transfusion was considered a new form of emergency surgery, carried out in order to save patients who could not be cured with traditional therapy of the time.

The aimed of this community service activity is to increase understanding of the village stakeholders, members of the BPD and community representatives in the village of Makamhaji, Sub District of Kartasura, District of Sukoharjo about the formulation of the Village Medium Development Planning (RPJMDesa) Document , this activity also provides assistance preparation of Medium Development Plan-Village formulation so that it appears the draft Development Plan-Village Makamhaji accordance with the Regulation of the Minister of Home Affair (MoHA) number 37 and 66 in the Year 2007. The methods used in these activities include training activities, discussions and document preparation assistance, the Village Development Plan implemented which directly implemented in the Village of Makamhaji. Training was conducted over 2 days at the Village Makamhaji, which was attended by 23 participants and technical assistance activity was conducted over 2 months after the training done to provide guidance and assistance in order to improve the preparation of the document draft of the Village Development Plan, this assistance activities take place in two places, firstly in department of Accounting FE UMS, and Village meeting room Makamhaji. As a result of training and technical assistance of planning preparation produced the draft of medium development plan document in Villages Makamhaji 2006 - 2012 which has been prepared jointly by the trainees with reference to source documents such as RPJMD Sukoharjo, Kartasura District Strategic Plan, Development Planning Consultation document (Musrenbang ) Makamhaji Village, District Kartasura Sukoharjo.

At present coin recognition and classification tasks are performed manually by the numismatists. This process requires lot of time and proper preservation of hard copies of source documents such as printed catalog. Hence automation in this area is very much helpful and supportive. In this paper machine vision based automatic coin recognition using local texture features such as SIFT (Scale Invariant Feature Transform) and SURF (Speed Up Robust Features) algorithms are proposed to complement the conventionalmanual classification and recognition process. This work is carried out on ancient Roman coins and the performance is compared.

Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents. We propose a weakly-supervised, model-based approach for verifying factual consistency and identifying conflicts between source documents and a generated summary. Training data is generated by applying a series of rule-based transformations to the sentences of source documents. The factual consistency model is then trained jointly for three tasks: 1) identify whether sentences remain factually consistent after transformation, 2) extract a span in the source documents to support the consistency prediction, 3) extract a span in the summary sentence that is inconsistent if one exists. Transferring this model to summaries generated by several state-of-the art models reveals that this highly scalable approach substantially outperforms previous models, including those trained with strong supervision using standard datasets for natural language inference and fact checking. Additionally, human evaluation shows that the auxiliary span extraction tasks provide useful assistance in the process of verifying factual consistency., 11 pages, 7 tables, 1 algorithm

This article reports and reflects on an element of a recent survey of UK public health professionals, specifically in relation to the Public Health Knowledge and Skills Framework (PHSKF) and the ethical requirements that underpin public health practice. Only 38.4% of respondents reported accessing the PHKSF and a mere 13.7% reported accessing the accompanying background paper on ethical public health practice. Given that ethical practice underpins the PHSKF, it is concerning that so few respondents are familiar with the PHSKF and one of the source documents. While issuing frameworks and guidance is one way to support public health practice, there is a further need for greater integration of skills and knowledge around ethical public health practice within education and training initiatives.

Background Every Month, health care providers submit copies of the MOH 731 reports to the SubCounty Health records and information officers. The data is then uploaded into the DHIS and becomes available for consumption by all the relevant stakeholders. Without a regular onsite audit of the data before uploading it, several errors may occur ranging from data entry transcription errors, arithmetic errors to outright inconsistencies with the source documents. Such errors render it unusable and incorrect. In order to correct this, Kajiado Central SubCounty, adopted a timely onsite data review and audit to identify and address the gaps reflected. Methods The SubCounty Health management team receives and reviews the monthly reports by the fifth of every month. Any gaps identified are either corrected instantly with the staff submitting the report or an on-site mentorship report is developed. Upon uploading, the DHIS reports are downloaded and cross-checked with the MoH 731 and the source documents for consistency. Results After a year of instituting such reviews, the data consistency between DHIS, MOH 731 and registers improved from 45% to 90%. The linkage and transitioning to ART for newly identified increased from 44.4% to 90%. Additionally, Maternal and infant prophylaxis improved from 99% and 88% to 97% and 100% respectively for those on PMTCT/ART however due to over reporting on maternal prophylaxis after cleaning there was a decline from 99% to 97%. N/B There is a graph which i was unable to attach here which i communicated same to Yurim Park. Conclusion Regular review of monthly reports at the collection point helps to quickly identify reporting gaps. Consequently, accurate gaps identification led to the correct remedial action on the technical areas and hence improvement on the indicators. Disclosure No significant relationships.