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Yes / The aim of this study is to profile the contemporary advanced clinical practitioner (ACP) role through standardized document sets. Documentary analysis of job descriptions (JDs), person specification and advertisements. England based jobs advertised on NHS jobs website from 22 January to 21 April 2021. A toatal of 143 trainee and qualified ACP roles were identified. A wide range of sectors and specialities were represented from across all English regions. The most common roles were urgent care, emergency medicine and primary care. Most qualified roles were agenda for change band 8A, although this did vary across specialities. Many roles were restricted to a small number of professions, notably nursing, physiotherapy and paramedic. Inconsistent role titles were noted. A lack of understanding of regulation across different professions was noted. The ACP role has become an accepted across healthcare providers in England. Implementation remains varied across specialities and organizations. Eligibility criteria may relate to professional bias. ACP roles are expanding but this may be at the detriment to advanced nursing posts. Inconsistency in role eligibility suggests some professional bias exists. This was scoping of ACP roles across England using job advertisements. ACP roles are common across sectors and specialities but eligibility varies. The research will have impact on those looking to recruit to ACP roles as well as those refining JDs. No EQUATOR guideline exists for document analysis. No Patient or Public Contribution. The research relates to organizational human resource information only.

A Visualização Computacional trata de técnicas para representar e interagir graficamente com dados complexos, em geral de alta dimensionalidade. Dados de alta dimensionalidade são caracterizados por pontos representados em espaços vetoriais de alta dimensão, cada coordenada representando um atributo do vetor. Num grande número de aplicações da visualização multidimensional uma medida de similaridade existe entre esses vetores. Técnicas de projeção multidimensional podem ser utilizadas para posicionamento desses dados num plano de forma a facilitar a interpretação das relações de similaridade. Entretanto alguns problemas dessas técnicas comprometem a interpretação dos resultados obtidos. Este trabalho identifica esses problemas e propõe, uma técnica para posicionar os pontos no plano, através da formação de árvores filogenéticas a partir de relações de similaridade. Em geral árvores filogenéticas são utilizadas para codificação de relações de ancestralidade. Um algoritmo de geração e um algoritmo de traçado dessas árvores foram implementados no contexto do sistema PEx (Projection Explorer) e a solução é comparada com a funcionalidade das projeções na interpretação de dados multidimensionais em geral e, em particular, na representação de coleções de documentos, uma aplicação bastante estratégica da visualização computacional e da mineração visual de dados / Computational Visualization is concerned with graphical representation and exploration of complex data, usually bearing high dimensionality. Multidimensional data are characterized by points represented in vector spaces of many dimensions, each coordinate representing an attribute of the vector. In many applications a similarity measure can be found to highlight relationships of proximity between the vectors. In this environment projection techniques offer an alternative to ease interpretation coded by the similarity measures through proximity on the display. They do so by positioning the points on a bidimensional plane. Projection techniques are very useful to display and interact with data, but present some drawbacks that in some cases compromise the interpretation of certain features in data sets. This work discusses such problems and proposes, as an alternative to represent similarity relationships and to provide point placement on a plane, the use of phylogenetic trees, a representation typically employed to represent ancestrality relationships. An algorithm for generation and an algorithm for drawing such trees were implemented in a system called Projection Explorer. The approach is compared to that of multidimensional projections for multidimensional data in general and, in particular, for document data sets, an strategic application for multidimensional visualizations, since text can be represented and interpreted as multi-dimensional entities

A Visualização Computacional trata de técnicas para representar e interagir graficamente com dados complexos, em geral de alta dimensionalidade. Dados de alta dimensionalidade são caracterizados por pontos representados em espaços vetoriais de alta dimensão, cada coordenada representando um atributo do vetor. Num grande número de aplicações da visualização multidimensional uma medida de similaridade existe entre esses vetores. Técnicas de projeção multidimensional podem ser utilizadas para posicionamento desses dados num plano de forma a facilitar a interpretação das relações de similaridade. Entretanto alguns problemas dessas técnicas comprometem a interpretação dos resultados obtidos. Este trabalho identifica esses problemas e propõe, uma técnica para posicionar os pontos no plano, através da formação de árvores filogenéticas a partir de relações de similaridade. Em geral árvores filogenéticas são utilizadas para codificação de relações de ancestralidade. Um algoritmo de geração e um algoritmo de traçado dessas árvores foram implementados no contexto do sistema PEx (Projection Explorer) e a solução é comparada com a funcionalidade das projeções na interpretação de dados multidimensionais em geral e, em particular, na representação de coleções de documentos, uma aplicação bastante estratégica da visualização computacional e da mineração visual de dados / Computational Visualization is concerned with graphical representation and exploration of complex data, usually bearing high dimensionality. Multidimensional data are characterized by points represented in vector spaces of many dimensions, each coordinate representing an attribute of the vector. In many applications a similarity measure can be found to highlight relationships of proximity between the vectors. In this environment projection techniques offer an alternative to ease interpretation coded by the similarity measures through proximity on the display. They do so by positioning the points on a bidimensional plane. Projection techniques are very useful to display and interact with data, but present some drawbacks that in some cases compromise the interpretation of certain features in data sets. This work discusses such problems and proposes, as an alternative to represent similarity relationships and to provide point placement on a plane, the use of phylogenetic trees, a representation typically employed to represent ancestrality relationships. An algorithm for generation and an algorithm for drawing such trees were implemented in a system called Projection Explorer. The approach is compared to that of multidimensional projections for multidimensional data in general and, in particular, for document data sets, an strategic application for multidimensional visualizations, since text can be represented and interpreted as multi-dimensional entities

Written notation has a long history in many musical traditions and has been particularly important in the composition and performance of Western art music. This study adopted the conceptual view that a musical score consists of two coordinated but separate communication channels: the musical text and a collection of composer-selected performance marks that serve as an interpretive gloss on that text. Structurally, these channels are defined by largely disjoint vocabularies of symbols and words. While the sound structures represented by musical texts are well studied in music theory and analysis, the stylistic patterns of performance marks and how they acquire contextual meaning in performance is an area with fewer theoretical foundations. This quantitative research explored the possibility that composers exhibit recurring patterns in their use of performance marks. Seventeen solo piano sonatas written between 1798 and 1913 by five major composers were analyzed from modern editions by tokenizing and tabulating the types and usage frequencies of their individual performance marks without regard to the associated musical texts. Using analytic methods common in information science, the results demonstrated persistent statistical similarities among the works of each composer and differences among the work groups of different composers. Although based on a small sample, the results still offered statistical support for the existence of recurring stylistic patterns in composers' use of performance marks across their works.

Despite decades of research, offline handwriting recognition (HWR) of historical documents remains a challenging problem, which if solved could greatly improve the searchability of online cultural heritage archives. Historical documents are plagued with noise, degradation, ink bleed-through, overlapping strokes, variation in slope and slant of the writing, and inconsistent layouts. Often the documents in a collection have been written by thousands of authors, all of whom have significantly different writing styles. In order to better capture the variations in writing styles we introduce a novel data augmentation technique. This methods achieves state-of-the-art results on modern datasets written in English and French and a historical dataset written in German.HWR models are often limited by the accuracy of the preceding steps of text detection and segmentation.Motivated by this, we present a deep learning model that jointly learns text detection, segmentation, and recognition using mostly images without detection or segmentation annotations.Our Start, Follow, Read (SFR) model is composed of a Region Proposal Network to find the start position of handwriting lines, a novel line follower network that incrementally follows and preprocesses lines of (perhaps curved) handwriting into dewarped images, and a CNN-LSTM network to read the characters. SFR exceeds the performance of the winner of the ICDAR2017 handwriting recognition competition, even when not using the provided competition region annotations.

Personal document management is the activity of managing a collection of digital documents performed by the owner of the documents, and consists of creation/acquisition, organisation, finding and maintenance. Document management is a pervasive aspect of digital work, but has received relatively little attention from researchers. The hierarchical file system used by most people to manage their documents has not conceptually changed in decades. Although revolutionary prototypes have been developed, these have not been grounded in a thorough understanding of document management behaviour and therefore have not resulted in significant changes to document management interfaces. Improvements in understanding document management can result in productivity gains for knowledge workers, and since document management is such a common activity, small improvements can deliver large gains. The aim of this research was to understand how people manage their personal document collections and to develop guidelines for the development of tools to support personal document management. A field study was conducted that included interviews, a survey and file system snapshot. The interviews were conducted with ten participants to investigate their document management strategies, structures and struggles. In addition to qualitative analysis of semi-structured interviews, a novel investigation technique was developed in the form of a file system snapshot which collects information about document structures and derives a number of metrics which describe the document structure. A survey was also conducted, consisting of a questionnaire and a file system snapshot, which enabled the findings of the field study to be validated, and to collect information from a greater number of participants. The results of this research culminated in (1) development of a conceptual framework highlighting the key personal document management attitudes, behaviours and concerns; (2) model of basic operations that any document management system needs to provide; (3) identification of piling, filing and structuring as three key document management strategies; (4) guidelines for the development of user interfaces to support document management, including specific guidelines for each document management strategy. These contributions both improve knowledge of personal document management on which future research can build, and provide practical advice to document management system designers which should result in the development of more usable system.

Personal document management systems provide good support for storing and organizing digital documents. However, there are no computer tools that support organization of paper documents on our desks. We ran a study of people's organization of their office desk space with respect to their digital workspace. This study resulted in a set of requirements for a media bridging tool. Based on these requirements, we built a prototype media bridging tool called PaperSpace that uses computer vision to link paper and digital documents. The system also tracks piles of paper documents on the real desktop, and links those papers to digital documents stored in the computer. Digital documents can be sorted and grouped according to the physical layout of the corresponding papers on the desk. The system automatically creates digital piles of documents in a simulated desktop that reflect the paper piles on the real desktop. The user can access valuable information through the system, such as printing statistics, location of a printed document on the desk, and past projects and their documents. A two week user evaluation of the system showed interesting usage scenarios and future trends for improving user interaction.

This thesis addresses difficult challenges in distributed document clustering and cluster summarization. Mining large document collections poses many challenges, one of which is the extraction of topics or summaries from documents for the purpose of interpretation of clustering results. Another important challenge, which is caused by new trends in distributed repositories and peer-to-peer computing, is that document data is becoming more distributed. We introduce a solution for interpreting document clusters using keyphrase extraction from multiple documents simultaneously. We also introduce two solutions for the problem of distributed document clustering in peer-to-peer environments, each satisfying a different goal: maximizing local clustering quality through collaboration, and maximizing global clustering quality through cooperation. The keyphrase extraction algorithm efficiently extracts and scores candidate keyphrases from a document cluster. The algorithm is called CorePhrase and is based on modeling document collections as a graph upon which we can leverage graph mining to extract frequent and significant phrases, which are used to label the clusters. Results show that CorePhrase can extract keyphrases relevant to documents in a cluster with very high accuracy. Although this algorithm can be used to summarize centralized clusters, it is specifically employed within distributed clustering to both boost distributed clustering accuracy, and to provide summaries for distributed clusters. The first method for distributed document clustering is called collaborative peer-to-peer document clustering, which models nodes in a peer-to-peer network as collaborative nodes with the goal of improving the quality of individual local clustering solutions. This is achieved through the exchange of local cluster summaries between peers, followed by recommendation of documents to be merged into remote clusters. Results on large sets of distributed document collections show that: (i) such collaboration technique achieves significant improvement in the final clustering of individual nodes; (ii) networks with larger number of nodes generally achieve greater improvements in clustering after collaboration relative to the initial clustering before collaboration, while on the other hand they tend to achieve lower absolute clustering quality than networks with fewer number of nodes; and (iii) as more overlap of the data is introduced across the nodes, collaboration tends to have little effect on improving clustering quality. The second method for distributed document clustering is called hierarchically-distributed document clustering. Unlike the collaborative model, this model aims at producing one clustering solution across the whole network. It specifically addresses scalability of network size, and consequently the distributed clustering complexity, by modeling the distributed clustering problem as a hierarchy of node neighborhoods. Summarization of the global distributed clusters is achieved through a distributed version of the CorePhrase algorithm. Results on large document sets show that: (i) distributed clustering accuracy is not affected by increasing the number of nodes for networks of single level; (ii) we can achieve decent speedup by making the hierarchy taller, but on the expense of clustering quality which degrades as we go up the hierarchy; (iii) in networks that grow arbitrarily, data gets more fragmented across neighborhoods causing poor centroid generation, thus suggesting we should not increase the number of nodes in the network beyond a certain level without increasing the data set size; and (iv) distributed cluster summarization can produce accurate summaries similar to those produced by centralized summarization. The proposed algorithms offer high degree of flexibility, scalability, and interpretability of large distributed document collections. Achieving the same results using current methodologies require centralization of the data first, which is sometimes not feasible.

Personal document management systems provide good support for storing and organizing digital documents. However, there are no computer tools that support organization of paper documents on our desks. We ran a study of people's organization of their office desk space with respect to their digital workspace. This study resulted in a set of requirements for a media bridging tool. Based on these requirements, we built a prototype media bridging tool called PaperSpace that uses computer vision to link paper and digital documents. The system also tracks piles of paper documents on the real desktop, and links those papers to digital documents stored in the computer. Digital documents can be sorted and grouped according to the physical layout of the corresponding papers on the desk. The system automatically creates digital piles of documents in a simulated desktop that reflect the paper piles on the real desktop. The user can access valuable information through the system, such as printing statistics, location of a printed document on the desk, and past projects and their documents. A two week user evaluation of the system showed interesting usage scenarios and future trends for improving user interaction.