Museums for all: translation and interpreting for multimodal spaces as a tool for universal accessibility

Audiovisual Translation (AVT) has a scientific responsibility to develop analytical methodologies for the textual phenomenon of multimodality, and for the translation strategies associated with it. At the same time, it should aim to provide studies of universal accessibility with a powerful tool for facilitating access to knowledge. This article offers some reflections on the theoretical foundations of AVT and considers how these are projected in the creation of new professional profiles, with specific application to universal accessibility in the museums.La Traducción Audiovisual (TAV) tiene la responsabilidad científica de desarrollar metodologías de análisis para el fenómeno textual de la multimodalidad así como para sus estrategias de traducción, a la vez que ha de proporcionar a los estudios en accesibilidad universal una poderosa herramienta de acceso al conocimiento. Este artículo ofrece reflexiones en torno a los fundamentos teóricos de la TAV y a la proyección de estos en nuevos perfiles profesionales; todo ello aplicado a la accesibilidad museística universal.This article is the English version of “Museos para todos. La traducción e interpretación para entornos multimodales como herramienta de accesibilidad universal” by Catalina Jiménez Hurtado, Claudia Seibel & Silvia Soler Gallego. It was not published on the print version of MonTI for reasons of space. The online version of MonTI does not suffer from these limitations, and this is our way of promoting plurilingualism.AMATRA Project (P07-SEJ/2660)

Are Good Intentions Good Enough?: Informed Consent Without Trained Interpreters

OBJECTIVE: To examine the informed consent process when trained language interpreters are unavailable. BACKGROUND: Ensuring sufficient patient understanding for informed consent is especially challenging for patients with Limited English Proficiency (LEP). While US law requires provision of competent translation for LEP patients, such services are commonly unavailable. DESIGN AND PARTICIPANTS: Qualitative data was collected in 8 prenatal genetics clinics in Texas, including interviews and observations with 16 clinicians, and 30 Latina patients. Using content analysis techniques, we examined whether the basic criteria for informed consent (voluntariness, discussion of alternatives, adequate information, and competence) were evident for each of these patients, contrasting LEP patients with patients not needing an interpreter. We present case examples of difficulties related to each of these criteria, and compare informed consent scores for consultations requiring interpretation and those which did not. RESULTS: We describe multiple communication problems related to the use of untrained interpreters, or reliance on clinicians’ own limited Spanish. These LEP patients appear to be consistently disadvantaged in each of the criteria we examined, and informed consent scores were notably lower for consultations which occurred across a language barrier. CONCLUSIONS: In the absence of adequate Spanish interpretation, it was uncertain whether these LEP patients were provided the quality and content of information needed to assure that they are genuinely informed. We offer some low-cost practice suggestions that might mitigate these problems, and improve the quality of language interpretation, which is essential to assuring informed choice in health care for LEP patients

An investigation into the understanding of basic genetic inheritance amongst amaXhosa caregivers of patients with Haemophilia

This study sought to explore the level of understanding of basic genetic inheritance among isiXhosa-speaking caregivers of patients with the genetic bleeding disorder haemophilia. Haemophilia A and B are X-linked recessive inherited, lifelong bleeding disorders that are caused by deficiencies in blood clotting factors. The condition predominantly affects males, while females are carriers and usually unaffected. These bleeding disorders have a profound impact on the daily life of the affected individual, on carrier mothers and close family. Education is vital to enable women to appreciate the implications of being a carrier and to fully inform them and their partners of the implications for a prospective child. Socio-economic and language issues in South Africa are a major barrier to communication and an obstacle to good medical care for first-language Xhosa speakers. In order to provide culturally-sensitive, effective genetic counselling and to improve communication between health care service providers and first-language Xhosa speakers, it is important to explore the intrinsic knowledge and basic understanding of this cultural group. The study used an exploratory qualitative research design. Ten participants were recruited amongst first-language Xhosa speaking mothers or caregivers of patients with haemophilia residing in townships near Cape Town. Qualitative data were generated from transcribed and translated audiorecords of ten semi-structured interviews, conducted in Xhosa by an interpreter as well as from participant observation notes by the investigator. Results suggest that the participants had a very limited understanding of the clinical management and cause of haemophilia. Information given by health care providers did not appear to be assimilated and participants remained unsure as to the implications of haemophilia. In spite of frequent visits to clinics there appeared to be limited understanding of the medical treatment and genetic consequences of haemophilia, which suggests that communication between health care providers and participants was inadequate. While treatment and care by health care service providers was fully accepted, several participants believe that traditional practices would provide more satisfactory explanations regarding the cause of the condition. Awareness by all role players of v different cultural beliefs and of how illness is interpreted by first-language Xhosa speakers might improve communication between health care service providers and isiXhosa speakers

Neural Network Guided Evolution of L-system Plants

A Lindenmayer system is a parallel rewriting system that generates graphic shapes using several rules. Genetic programming (GP) is an evolutionary algorithm that evolves expressions. A convolutional neural network(CNN) is a type of neural network which is useful for image recognition and classification. The goal of this thesis will be to generate different styles of L-system based 2D images of trees from scratch using genetic programming. The system will use a convolutional neural network to evaluate the trees and produce a fitness value for genetic programming. Different architectures of CNN are explored. We analyze the performance of the system and show the capabilities of the combination of CNN and GP. We show that a variety of interesting tree images can be automatically evolved. We also found that the success of the system highly depends on CNN training, as well as the form of the GP's L-system language representation

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde

The language of risk and the risk of language

This mini-dissertation is written towards an MA in Linguistics. As such, it falls within the domain of Humanities. However, its author is a doctor and the subject matter is medical, which falls within the domain of Science. The mini-dissertation thus bridges these different domains, and the references and background reading as well as the application of the research reflect this hybrid nature. A glossary of medical terms and acronyms is thus given. In addition to being a doctor, the author is also a mother of two children. Thus the subject matter of pregnancy and its unknowns is close to her professional and personal realms of experience. For this reason, although the author has tried to be objective, she cannot pretend that true objectivity is always achieved

An explicitly structured control model for exploring search space: chorale harmonisation in the style of J.S. Bach

In this research, we present our computational model which performs four part har-monisation in the style of J.S. Bach. Harmonising Bach chorales is a hard AI problem, comparable to natural language understanding. In our approach, we explore the issue of gaining control in an explicit way for the chorale harmonisation tasks. Generally, the control over the search space may be from both domain dependent and domain inde-pendent control knowledge. Our explicit control emphasises domain dependent control knowledge. The control gained from domain d ependent control enables us to map a clearer relationship between the control applied and its effects. Two examples of do-main dependent control are a plan of tasks to be done and heuristics stating properties of the domain. Examples of domain independent control are notions such as temperature values in an annealing method; mutation rates in Genetic Algorithms; and weights in Artificial Neural Networks.The appeal of the knowledge based approach lies in the accessibility to the control if required. Our system exploits this concept extensively. Control is explicitly expressed by weaving different atomic definitions {i.e. the rules, tests and measures) together with appropriate control primitives. Each expression constructed is called a control definition, which is hierarchical by nature.One drawback of the knowledge based approach is that, as the system grows bigger, the exploitation of the new added knowledge grows exponentially. This leads to an intractable search space. To reduce this intractability problem, we partially search the search space at the meta-level. This meta-level architecture reduces the complexity in the search space by exploiting search at the meta-level which has a smaller search space.The experiment shows that an explicitly structured control offers a greater flexibility in controlling the search space as it allows the control definitions to be manipulated and modified with great flexibility. This is a crucial clement in performing partial search over a big search space. As the control is allowed to be examined, the system also potentially supports elaborate explanations of the system activities and reflections at the meta-level

A Framework for File Format Fuzzing with Genetic Algorithms

Secure software, meaning software free from vulnerabilities, is desirable in today\u27s marketplace. Consumers are beginning to value a product\u27s security posture as well as its functionality. Software development companies are recognizing this trend, and they are factoring security into their entire software development lifecycle. Secure development practices like threat modeling, static analysis, safe programming libraries, run-time protections, and software verification are being mandated during product development. Mandating these practices improves a product\u27s security posture before customer delivery, and these practices increase the difficulty of discovering and exploiting vulnerabilities. Since the 1980\u27s, security researchers have uncovered software defects by fuzz testing an application. In fuzz testing\u27s infancy, randomly generated data could discover multiple defects quickly. However, as software matures and software development companies integrate secure development practices into their development life cycles, fuzzers must apply more sophisticated techniques in order to retain their ability to uncover defects. Fuzz testing must evolve, and fuzz testing practitioners must devise new algorithms to exercise an application in unexpected ways. This dissertation\u27s objective is to create a proof-of-concept genetic algorithm fuzz testing framework to exercise an application\u27s file format parsing routines. The framework includes multiple genetic algorithm variations, provides a configuration scheme, and correlates data gathered from static and dynamic analysis to guide negative test case evolution. Experiments conducted for this dissertation illustrate the effectiveness of a genetic algorithm fuzzer in comparison to standard fuzz testing tools. The experiments showcase a genetic algorithm fuzzer\u27s ability to discover multiple unique defects within a limited number of negative test cases. These experiments also highlight an application\u27s increased execution time when fuzzing with a genetic algorithm. To combat increased execution time, a distributed architecture is implemented and additional experiments demonstrate a decrease in execution time comparable to standard fuzz testing tools. A final set of experiments provide guidance on fitness function selection with a CHC genetic algorithm fuzzer with different population size configurations

Aerospace Medicine and Biology: A continuing bibliography, supplement 191

A bibliographical list of 182 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1979 is presented