title: Models for computer generated parody

authors: Smith, Tony C.

subjects: Machine learning

abstract: This paper outlines two approaches to the construction of computer systems that generate prose in the style of a given author. The first involves using intuitive notions of stylistic trademarks to construct a grammar that characterizes a particular author in this case, Ernest Hemingway. The second uses statistical methods for inferring a grammar from samples of an author's work in this instance, Thomas Hardy. A brief outline of grammar induction principles is included as background material for the latter system. The relative merits of each approach are discussed, and text generated from the resulting grammars is assessed in terms of its parodic quality. Further to its esoteric interest, a discussion of parody generation as a useful technique for measuring the success of grammatical inferencing systems is included, along with suggestions for its practical application in areas of language modeling and text compression.

text: Models for computer generated parody This paper outlines two approaches to the construction of computer systems that generate prose in the style of a given author. The first involves using intuitive notions of stylistic trademarks to construct a grammar that characterizes a particular author in this case, Ernest Hemingway. The second uses statistical methods for inferring a grammar from samples of an author's work in this instance, Thomas Hardy. A brief outline of grammar induction principles is included as background material for the latter system. The relative merits of each approach are discussed, and text generated from the resulting grammars is assessed in terms of its parodic quality. Further to its esoteric interest, a discussion of parody generation as a useful technique for measuring the success of grammatical inferencing systems is included, along with suggestions for its practical application in areas of language modeling and text compression.

year: 1993

title: Enhancing Clinical Decision Support Systems through Hospital Information System Integration and Machine Learning in a Context of the Emergency Department

authors: Jiang, Hongwei

abstract: This study conducts design science research (DSR) aimed at addressing the challenge of improving clinical decision support (CDS) in Emergency Departments (EDs). Making timely decisions by physicians to meet the diverse demands within hospital EDs is a challenge as the inadequacies of current Hospital Information Systems (HIS) can host fragmented Electronic Health Record (EHR) data across multiple sub-systems. The advancement of Information Technology (IT), notably machine learning (ML), brings new initiatives for addressing this issue. To tackle this complexity, this research deploys DSR to design a novel system integration architecture. This architecture provides a comprehensive framework that orchestrates HIS integration across multiple strata, encompassing the business, application, and technology layers, to facilitate the integration of ML-based Clinical Decision Support Systems (CDSS) within the ED setting. Two design artefacts are developed as outcomes of DSR: firstly, the development of the system integration architecture, and secondly, the creation of the ML-based CDSS. The former serves as a meticulously designed blueprint for HIS integration, ensuring the effective functioning of ML-based CDSS at the point of care. The latter represents a pioneering CDSS system, harnessing the power of ML algorithms to furnish real-time, context-aware decision support. The impact of the ML-based CDSS on ED efficiency is subjected to rigorous evaluation. This evaluation is conducted by leveraging historical Electronic Health Record (EHR) data within a simulated ED environment. The simulation, calibrated with parameters drawn from a real-world hospital ED setting, yields promising results. These findings underscore the feasibility and the manifold benefits of integrating ML-based CDSS to augment ED efficiency. Furthermore, this research makes a noteworthy contribution to the theoretical underpinnings of information system design. It achieves this by pioneering the development of a novel system integration architecture. This architecture serves as a bridge, alleviating the knowledge gap that traditionally separates HIS integration from ML-based CDSS. The study advances the understanding of strategic design and integration of hospital information systems to support ML and CDSS. Ultimately, this advancement holds the potential to catalyse substantial improvements in patient care and outcomes, especially within the intricate and high-stakes environment of the Emergency Department.

text: Enhancing Clinical Decision Support Systems through Hospital Information System Integration and Machine Learning in a Context of the Emergency Department This study conducts design science research (DSR) aimed at addressing the challenge of improving clinical decision support (CDS) in Emergency Departments (EDs). Making timely decisions by physicians to meet the diverse demands within hospital EDs is a challenge as the inadequacies of current Hospital Information Systems (HIS) can host fragmented Electronic Health Record (EHR) data across multiple sub-systems. The advancement of Information Technology (IT), notably machine learning (ML), brings new initiatives for addressing this issue. To tackle this complexity, this research deploys DSR to design a novel system integration architecture. This architecture provides a comprehensive framework that orchestrates HIS integration across multiple strata, encompassing the business, application, and technology layers, to facilitate the integration of ML-based Clinical Decision Support Systems (CDSS) within the ED setting. Two design artefacts are developed as outcomes of DSR: firstly, the development of the system integration architecture, and secondly, the creation of the ML-based CDSS. The former serves as a meticulously designed blueprint for HIS integration, ensuring the effective functioning of ML-based CDSS at the point of care. The latter represents a pioneering CDSS system, harnessing the power of ML algorithms to furnish real-time, context-aware decision support. The impact of the ML-based CDSS on ED efficiency is subjected to rigorous evaluation. This evaluation is conducted by leveraging historical Electronic Health Record (EHR) data within a simulated ED environment. The simulation, calibrated with parameters drawn from a real-world hospital ED setting, yields promising results. These findings underscore the feasibility and the manifold benefits of integrating ML-based CDSS to augment ED efficiency. Furthermore, this research makes a noteworthy contribution to the theoretical underpinnings of information system design. It achieves this by pioneering the development of a novel system integration architecture. This architecture serves as a bridge, alleviating the knowledge gap that traditionally separates HIS integration from ML-based CDSS. The study advances the understanding of strategic design and integration of hospital information systems to support ML and CDSS. Ultimately, this advancement holds the potential to catalyse substantial improvements in patient care and outcomes, especially within the intricate and high-stakes environment of the Emergency Department.

year: 2024

title: Contextualizing Indigenous people and the state of exception: New Zealand's Waikeria Prison protest

authors: Martin, Jessica

abstract: On Tuesday, December 29, 2020, 16 protesters at Waikeria Prison, one of New Zealand's largest male prisons, engaged in a six-day standoff with prison guards to protest inhumane prison conditions. The Department of Corrections framed the event as an aimless riot, delegitimizing the intentional demonstration of resistance against state violence. Imprisoned intellectuals, specifically Imprisoned Black Radical tradition, have long examined and centered the prison as the harshest instrument of the state linked to the struggles of the collective. However, voices from imprisoned intellectuals are rarely considered in academic scholarship despite acute analysis of the state and liberation. This article employs the state of exception to contextualize the Waikeria protest. Particular attention is devoted to the ‘state of continuity,’ which allows for a broader understanding of a permanent state of racialized oppression and marginalization faced by Indigenous and Black communities in racialized-settler-colonial contexts. Populations designated as the exception are thus framed as a threat targeted for militaristic police intervention. This article concludes by extending the discussion of the state of continuity to include how expressions of rage and dissent by Indigenous and Black people are viewed as a direct threat to the sovereign order but are necessary for revolutionary change.

text: Contextualizing Indigenous people and the state of exception: New Zealand's Waikeria Prison protest On Tuesday, December 29, 2020, 16 protesters at Waikeria Prison, one of New Zealand's largest male prisons, engaged in a six-day standoff with prison guards to protest inhumane prison conditions. The Department of Corrections framed the event as an aimless riot, delegitimizing the intentional demonstration of resistance against state violence. Imprisoned intellectuals, specifically Imprisoned Black Radical tradition, have long examined and centered the prison as the harshest instrument of the state linked to the struggles of the collective. However, voices from imprisoned intellectuals are rarely considered in academic scholarship despite acute analysis of the state and liberation. This article employs the state of exception to contextualize the Waikeria protest. Particular attention is devoted to the ‘state of continuity,’ which allows for a broader understanding of a permanent state of racialized oppression and marginalization faced by Indigenous and Black communities in racialized-settler-colonial contexts. Populations designated as the exception are thus framed as a threat targeted for militaristic police intervention. This article concludes by extending the discussion of the state of continuity to include how expressions of rage and dissent by Indigenous and Black people are viewed as a direct threat to the sovereign order but are necessary for revolutionary change.

year: 2024-05-16