736 research outputs found

    On the real world practice of Behaviour Driven Development

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    Surveys of industry practice over the last decade suggest that Behaviour Driven Development is a popular Agile practice. For example, 19% of respondents to the 14th State of Agile annual survey reported using BDD, placing it in the top 13 practices reported. As well as potential benefits, the adoption of BDD necessarily involves an additional cost of writing and maintaining Gherkin features and scenarios, and (if used for acceptance testing,) the associated step functions. Yet there is a lack of published literature exploring how BDD is used in practice and the challenges experienced by real world software development efforts. This gap is significant because without understanding current real world practice, it is hard to identify opportunities to address and mitigate challenges. In order to address this research gap concerning the challenges of using BDD, this thesis reports on a research project which explored: (a) the challenges of applying agile and undertaking requirements engineering in a real world context; (b) the challenges of applying BDD specifically and (c) the application of BDD in open-source projects to understand challenges in this different context. For this purpose, we progressively conducted two case studies, two series of interviews, four iterations of action research, and an empirical study. The first case study was conducted in an avionics company to discover the challenges of using an agile process in a large scale safety critical project environment. Since requirements management was found to be one of the biggest challenges during the case study, we decided to investigate BDD because of its reputation for requirements management. The second case study was conducted in the company with an aim to discover the challenges of using BDD in real life. The case study was complemented with an empirical study of the practice of BDD in open source projects, taking a study sample from the GitHub open source collaboration site. As a result of this Ph.D research, we were able to discover: (i) challenges of using an agile process in a large scale safety-critical organisation, (ii) current state of BDD in practice, (iii) technical limitations of Gherkin (i.e., the language for writing requirements in BDD), (iv) challenges of using BDD in a real project, (v) bad smells in the Gherkin specifications of open source projects on GitHub. We also presented a brief comparison between the theoretical description of BDD and BDD in practice. This research, therefore, presents the results of lessons learned from BDD in practice, and serves as a guide for software practitioners planning on using BDD in their projects

    A clinical decision support system for detecting and mitigating potentially inappropriate medications

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    Background: Medication errors are a leading cause of preventable harm to patients. In older adults, the impact of ageing on the therapeutic effectiveness and safety of drugs is a significant concern, especially for those over 65. Consequently, certain medications called Potentially Inappropriate Medications (PIMs) can be dangerous in the elderly and should be avoided. Tackling PIMs by health professionals and patients can be time-consuming and error-prone, as the criteria underlying the definition of PIMs are complex and subject to frequent updates. Moreover, the criteria are not available in a representation that health systems can interpret and reason with directly. Objectives: This thesis aims to demonstrate the feasibility of using an ontology/rule-based approach in a clinical knowledge base to identify potentially inappropriate medication(PIM). In addition, how constraint solvers can be used effectively to suggest alternative medications and administration schedules to solve or minimise PIM undesirable side effects. Methodology: To address these objectives, we propose a novel integrated approach using formal rules to represent the PIMs criteria and inference engines to perform the reasoning presented in the context of a Clinical Decision Support System (CDSS). The approach aims to detect, solve, or minimise undesirable side-effects of PIMs through an ontology (knowledge base) and inference engines incorporating multiple reasoning approaches. Contributions: The main contribution lies in the framework to formalise PIMs, including the steps required to define guideline requisites to create inference rules to detect and propose alternative drugs to inappropriate medications. No formalisation of the selected guideline (Beers Criteria) can be found in the literature, and hence, this thesis provides a novel ontology for it. Moreover, our process of minimising undesirable side effects offers a novel approach that enhances and optimises the drug rescheduling process, providing a more accurate way to minimise the effect of drug interactions in clinical practice

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Exploring Cognitive Biases in Pain: Investigating Attention, Interpretation and Memory Bias..

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    Cognitive-affective models posit that cognitive biases contribute to the aetiology and maintenance of chronic pain. In chronic pain, it is argued that cognitive biases encapsulate interpretation bias, attentional bias, and memory bias. These biases are suggested to exert their influence through the preferential processing of information pertaining to pain, bodily-threat, and harm. Research exploring multiple cognitive biases within the context of a single study is limited. Thus, the role, nature and interaction of these cognitive biases remains poorly understood. This programme of research aimed to address these limitations. Studies 1 and 2 progressed the development and validation of stimulus sets suitable for measuring pain-related attention and interpretation biases in adults. Study 3 then investigated whether a single experience of pain influences cognitive biases in a pain-free sample subjected to acute pain; and study 4 investigated the measurement of cognitive biases, in a chronic pain (vs. non-pain control) sample. Study 1 resulted in the development of two stimulus sets categorised via varying degrees of pain intensity (neutral, low, high) and threat (low, medium, high) to enable rigorous investigation of attentional bias. Study 2 resulted in the development and validation of two ambiguous scenario stimulus sets to enable rigorous investigation of interpretation (and subsequently memory) bias utilising i) forced-choice and ii) free-response paradigms. Supplementary analyses indicated that recent pain experiences positively correlated with the endorsement of pain/pain-illness interpretations of the ambiguous scenarios. Study 3 revealed that a single acute pain experience was not sufficient to influence cognitive biases. However, individuals subjected to a warm water control (as opposed to a cold-pressor task) showed increased attention towards pain-related information, increased recall of pain words immediately following the warm water control, and greater recognition of non-pain words. Additionally, in the acute pain group, measures of pain threshold and tolerance were associated with attention, interpretation, and memory biases. These results indicate a potentially pleasant experience can bias attention toward pain stimulus processing and the importance of pain sensitivity as an influencing cognitive bias factor. Consistent with Study 3, Study 4 provided no evidence of pain-related interpretation or recall biases. However, the chronic pain group exhibited poorer overall recognition performance, compared to their pain-free counterparts. Cross-bias correlations further revealed that as the number of ambiguous scenarios interpreted as pain/pain-illness related increased, so too did the number of pain/pain-illness solutions correctly recalled, irrespective of pain experience. However, correlations between cognitive biases for the non-pain/non-pain illness stimuli were exclusive to the pain-free group. This indicates that the chronic pain group processed scenarios interpreted in a pain/pain-illness manner differently than those they interpreted in a non-pain/non-pain illness manner. Overarching conclusions indicate that individuals with lower pain thresholds and tolerance are more likely to display biased attention, interpretation, and memory favouring pain/pain-illness information; and that individuals with chronic pain display impaired recognition for pain/pain-illness related information. A detailed discussion of these findings is presented in the final chapter, including the proposition of a Pain Sensitivity Model in understanding the role of cognitive biases in pain

    Explainable temporal data mining techniques to support the prediction task in Medicine

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    In the last decades, the increasing amount of data available in all fields raises the necessity to discover new knowledge and explain the hidden information found. On one hand, the rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, results to users. In the biomedical informatics and computer science communities, there is considerable discussion about the `` un-explainable" nature of artificial intelligence, where often algorithms and systems leave users, and even developers, in the dark with respect to how results were obtained. Especially in the biomedical context, the necessity to explain an artificial intelligence system result is legitimate of the importance of patient safety. On the other hand, current database systems enable us to store huge quantities of data. Their analysis through data mining techniques provides the possibility to extract relevant knowledge and useful hidden information. Relationships and patterns within these data could provide new medical knowledge. The analysis of such healthcare/medical data collections could greatly help to observe the health conditions of the population and extract useful information that can be exploited in the assessment of healthcare/medical processes. Particularly, the prediction of medical events is essential for preventing disease, understanding disease mechanisms, and increasing patient quality of care. In this context, an important aspect is to verify whether the database content supports the capability of predicting future events. In this thesis, we start addressing the problem of explainability, discussing some of the most significant challenges need to be addressed with scientific and engineering rigor in a variety of biomedical domains. We analyze the ``temporal component" of explainability, focusing on detailing different perspectives such as: the use of temporal data, the temporal task, the temporal reasoning, and the dynamics of explainability in respect to the user perspective and to knowledge. Starting from this panorama, we focus our attention on two different temporal data mining techniques. The first one, based on trend abstractions, starting from the concept of Trend-Event Pattern and moving through the concept of prediction, we propose a new kind of predictive temporal patterns, namely Predictive Trend-Event Patterns (PTE-Ps). The framework aims to combine complex temporal features to extract a compact and non-redundant predictive set of patterns composed by such temporal features. The second one, based on functional dependencies, we propose a methodology for deriving a new kind of approximate temporal functional dependencies, called Approximate Predictive Functional Dependencies (APFDs), based on a three-window framework. We then discuss the concept of approximation, the data complexity of deriving an APFD, the introduction of two new error measures, and finally the quality of APFDs in terms of coverage and reliability. Exploiting these methodologies, we analyze intensive care unit data from the MIMIC dataset

    Application of knowledge management principles to support maintenance strategies in healthcare organisations

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    Healthcare is a vital service that touches people's lives on a daily basis by providing treatment and resolving patients' health problems through the staff. Human lives are ultimately dependent on the skilled hands of the staff and those who manage the infrastructure that supports the daily operations of the service, making it a compelling reason for a dedicated research study. However, the UK healthcare sector is undergoing rapid changes, driven by rising costs, technological advancements, changing patient expectations, and increasing pressure to deliver sustainable healthcare. With the global rise in healthcare challenges, the need for sustainable healthcare delivery has become imperative. Sustainable healthcare delivery requires the integration of various practices that enhance the efficiency and effectiveness of healthcare infrastructural assets. One critical area that requires attention is the management of healthcare facilities. Healthcare facilitiesis considered one of the core elements in the delivery of effective healthcare services, as shortcomings in the provision of facilities management (FM) services in hospitals may have much more drastic negative effects than in any other general forms of buildings. An essential element in healthcare FM is linked to the relationship between action and knowledge. With a full sense of understanding of infrastructural assets, it is possible to improve, manage and make buildings suitable to the needs of users and to ensure the functionality of the structure and processes. The premise of FM is that an organisation's effectiveness and efficiency are linked to the physical environment in which it operates and that improving the environment can result in direct benefits in operational performance. The goal of healthcare FM is to support the achievement of organisational mission and goals by designing and managing space and infrastructural assets in the best combination of suitability, efficiency, and cost. In operational terms, performance refers to how well a building contributes to fulfilling its intended functions. Therefore, comprehensive deployment of efficient FM approaches is essential for ensuring quality healthcare provision while positively impacting overall patient experiences. In this regard, incorporating knowledge management (KM) principles into hospitals' FM processes contributes significantly to ensuring sustainable healthcare provision and enhancement of patient experiences. Organisations implementing KM principles are better positioned to navigate the constantly evolving business ecosystem easily. Furthermore, KM is vital in processes and service improvement, strategic decision-making, and organisational adaptation and renewal. In this regard, KM principles can be applied to improve hospital FM, thereby ensuring sustainable healthcare delivery. Knowledge management assumes that organisations that manage their organisational and individual knowledge more effectively will be able to cope more successfully with the challenges of the new business ecosystem. There is also the argument that KM plays a crucial role in improving processes and services, strategic decision-making, and adapting and renewing an organisation. The goal of KM is to aid action – providing "a knowledge pull" rather than the information overload most people experience in healthcare FM. Other motivations for seeking better KM in healthcare FM include patient safety, evidence-based care, and cost efficiency as the dominant drivers. The most evidence exists for the success of such approaches at knowledge bottlenecks, such as infection prevention and control, working safely, compliances, automated systems and reminders, and recall based on best practices. The ability to cultivate, nurture and maximise knowledge at multiple levels and in multiple contexts is one of the most significant challenges for those responsible for KM. However, despite the potential benefits, applying KM principles in hospital facilities is still limited. There is a lack of understanding of how KM can be effectively applied in this context, and few studies have explored the potential challenges and opportunities associated with implementing KM principles in hospitals facilities for sustainable healthcare delivery. This study explores applying KM principles to support maintenance strategies in healthcare organisations. The study also explores the challenges and opportunities, for healthcare organisations and FM practitioners, in operationalising a framework which draws the interconnectedness between healthcare. The study begins by defining healthcare FM and its importance in the healthcare industry. It then discusses the concept of KM and the different types of knowledge that are relevant in the healthcare FM sector. The study also examines the challenges that healthcare FM face in managing knowledge and how the application of KM principles can help to overcome these challenges. The study then explores the different KM strategies that can be applied in healthcare FM. The KM benefits include improved patient outcomes, reduced costs, increased efficiency, and enhanced collaboration among healthcare professionals. Additionally, issues like creating a culture of innovation, technology, and benchmarking are considered. In addition, a framework that integrates the essential concepts of KM in healthcare FM will be presented and discussed. The field of KM is introduced as a complex adaptive system with numerous possibilities and challenges. In this context, and in consideration of healthcare FM, five objectives have been formulated to achieve the research aim. As part of the research, a number of objectives will be evaluated, including appraising the concept of KM and how knowledge is created, stored, transferred, and utilised in healthcare FM, evaluating the impact of organisational structure on job satisfaction as well as exploring how cultural differences impact knowledge sharing and performance in healthcare FM organisations. This study uses a combination of qualitative methods, such as meetings, observations, document analysis (internal and external), and semi-structured interviews, to discover the subjective experiences of healthcare FM employees and to understand the phenomenon within a real-world context and attitudes of healthcare FM as the data collection method, using open questions to allow probing where appropriate and facilitating KM development in the delivery and practice of healthcare FM. The study describes the research methodology using the theoretical concept of the "research onion". The qualitative research was conducted in the NHS acute and non-acute hospitals in Northwest England. Findings from the research study revealed that while the concept of KM has grown significantly in recent years, KM in healthcare FM has received little or no attention. The target population was fifty (five FM directors, five academics, five industry experts, ten managers, ten supervisors, five team leaders and ten operatives). These seven groups were purposively selected as the target population because they play a crucial role in KM enhancement in healthcare FM. Face-to-face interviews were conducted with all participants based on their pre-determined availability. Out of the 50-target population, only 25 were successfully interviewed to the point of saturation. Data collected from the interview were coded and analysed using NVivo to identify themes and patterns related to KM in healthcare FM. The study is divided into eight major sections. First, it discusses literature findings regarding healthcare FM and KM, including underlying trends in FM, KM in general, and KM in healthcare FM. Second, the research establishes the study's methodology, introducing the five research objectives, questions and hypothesis. The chapter introduces the literature on methodology elements, including philosophical views and inquiry strategies. The interview and data analysis look at the feedback from the interviews. Lastly, a conclusion and recommendation summarise the research objectives and suggest further research. Overall, this study highlights the importance of KM in healthcare FM and provides insights for healthcare FM directors, managers, supervisors, academia, researchers and operatives on effectively leveraging knowledge to improve patient care and organisational effectiveness

    What do rendering options tell us about the translating mind? Testing the choice network analysis hypothesis

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    Frame. Assessing the difficulty of source texts and parts thereof is important in CTIS, whether for research comparability, for didactic purposes or setting price differences in the market. In order to empirically measure it, Campbell & Hale (1999) and Campbell (2000) developed the Choice Network Analysis (CNA) framework. Basically, the CNA’s main hypothesis is that the more translation options (a group of) translators have to render a given source text stretch, the higher the difficulty of that text stretch will be. We will call this the CNA hypothesis. In a nutshell, this research project puts the CNA hypothesis to the test and studies whether it does actually measure difficulty. Data collection. Two groups of participants (n=29) of different profiles and from two universities in different countries had three translation tasks keylogged with Inputlog, and filled pre- and post-translation questionnaires. Participants translated from English (L2) into their L1s (Spanish or Italian), and worked—first in class and then at home—using their own computers, on texts ca. 800–1000 words long. Each text was translated in approximately equal halves in two 1-hour sessions, in three consecutive weeks. Only the parts translated at home were considered in the study. Results. A very different picture emerged from data than that which the CNA hypothesis might predict: there was no prevalence of disfluent task segments when there were many translation options, nor was a prevalence of fluent task segments associated to fewer translation options. Indeed, there was no correlation between the number of translation options (many and few) and behavioral fluency. Additionally, there was no correlation between pauses and both behavioral fluency and typing speed. The discussed theoretical flaws and the empirical evidence lead to the conclusion that the CNA framework does not and cannot measure text and translation difficulty.Stato dell'arte. La valutazione della difficoltà dei testi di partenza e di parti di essi ricopre un ruolo centrale nel campo degli studi cognitivi sulla traduzione e l'interpretazione (CTIS). Per misurarla a livello empirico, Campbell & Hale (1999) e Campbell (2000) hanno sviluppato la Choice Network Analysis (CNA). L'ipotesi principale della CNA è che quante più opzioni di traduzione un gruppo di traduttori ha per tradurre una porzione di testo, più alta sarà la sua difficoltà. Questo progetto di ricerca mette alla prova l'ipotesi della CNA per verificarne la validità come strumento per misurare la difficoltà. Raccolta dei dati. Due gruppi di partecipanti (n=29) di profili diversi e provenienti da due università di paesi diversi hanno svolto tre prove di traduzione usando Inputlog, ognuna preceduta e seguita da un questionario. I partecipanti hanno tradotto dall'inglese (L2) alla loro L1 (spagnolo o italiano) e hanno lavorato prima in classe e poi a casa con i propri computer su testi di circa 800-1000 parole. Ogni testo è stato suddiviso in metà pressoché uguali e tradotto in due sessioni da un'ora l'una, in tre settimane consecutive. Risultati. Dai dati è emerso un quadro molto diverso da quello suggerito dall'ipotesi della CNA: non è stata riscontrata alcuna prevalenza di segmenti con minore fluidità relativi a un maggior numero di opzioni di traduzione, né una prevalenza di segmenti con maggiore fluidità associati a un minor numero di opzioni di traduzione. Al contrario, in entrambi i casi la fluidità dei segmenti è rimasta tendenzialmente nella media. Infine, non è stata riscontrata alcuna correlazione tra le pause e fluidità comportamentale o la velocità di batttitura. Le inesattezze teoriche precedentemente discusse e le prove empiriche portano alla conclusione che la CNA non misura e non può misurare la difficoltà del testo e della traduzione

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics
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