9,163 research outputs found

    Bayesian networks for disease diagnosis: What are they, who has used them and how?

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    A Bayesian network (BN) is a probabilistic graph based on Bayes' theorem, used to show dependencies or cause-and-effect relationships between variables. They are widely applied in diagnostic processes since they allow the incorporation of medical knowledge to the model while expressing uncertainty in terms of probability. This systematic review presents the state of the art in the applications of BNs in medicine in general and in the diagnosis and prognosis of diseases in particular. Indexed articles from the last 40 years were included. The studies generally used the typical measures of diagnostic and prognostic accuracy: sensitivity, specificity, accuracy, precision, and the area under the ROC curve. Overall, we found that disease diagnosis and prognosis based on BNs can be successfully used to model complex medical problems that require reasoning under conditions of uncertainty.Comment: 22 pages, 5 figures, 1 table, Student PhD first pape

    UniverSeg: Universal Medical Image Segmentation

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    While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task, researchers generally have to train or fine-tune models, which is time-consuming and poses a substantial barrier for clinical researchers, who often lack the resources and expertise to train neural networks. We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. Given a query image and example set of image-label pairs that define a new segmentation task, UniverSeg employs a new Cross-Block mechanism to produce accurate segmentation maps without the need for additional training. To achieve generalization to new tasks, we have gathered and standardized a collection of 53 open-access medical segmentation datasets with over 22,000 scans, which we refer to as MegaMedical. We used this collection to train UniverSeg on a diverse set of anatomies and imaging modalities. We demonstrate that UniverSeg substantially outperforms several related methods on unseen tasks, and thoroughly analyze and draw insights about important aspects of the proposed system. The UniverSeg source code and model weights are freely available at https://universeg.csail.mit.eduComment: Victor and Jose Javier contributed equally to this work. Project Website: https://universeg.csail.mit.ed

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    Corporate Social Responsibility: the institutionalization of ESG

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    Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective

    Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?

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    The use of pretrained embeddings has become widespread in modern e-commerce machine learning (ML) systems. In practice, however, we have encountered several key issues when using pretrained embedding in a real-world production system, many of which cannot be fully explained by current knowledge. Unfortunately, we find that there is a lack of a thorough understanding of how pre-trained embeddings work, especially their intrinsic properties and interactions with downstream tasks. Consequently, it becomes challenging to make interactive and scalable decisions regarding the use of pre-trained embeddings in practice. Our investigation leads to two significant discoveries about using pretrained embeddings in e-commerce applications. Firstly, we find that the design of the pretraining and downstream models, particularly how they encode and decode information via embedding vectors, can have a profound impact. Secondly, we establish a principled perspective of pre-trained embeddings via the lens of kernel analysis, which can be used to evaluate their predictability, interactively and scalably. These findings help to address the practical challenges we faced and offer valuable guidance for successful adoption of pretrained embeddings in real-world production. Our conclusions are backed by solid theoretical reasoning, benchmark experiments, as well as online testings

    An Experimental Study on Sentiment Classification of Moroccan dialect texts in the web

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    With the rapid growth of the use of social media websites, obtaining the users' feedback automatically became a crucial task to evaluate their tendencies and behaviors online. Despite this great availability of information, and the increasing number of Arabic users only few research has managed to treat Arabic dialects. The purpose of this paper is to study the opinion and emotion expressed in real Moroccan texts precisely in the YouTube comments using some well-known and commonly used methods for sentiment analysis. In this paper, we present our work of Moroccan dialect comments classification using Machine Learning (ML) models and based on our collected and manually annotated YouTube Moroccan dialect dataset. By employing many text preprocessing and data representation techniques we aim to compare our classification results utilizing the most commonly used supervised classifiers: k-nearest neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and deep learning (DL) classifiers such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LTSM). Experiments were performed using both raw and preprocessed data to show the importance of the preprocessing. In fact, the experimental results prove that DL models have a better performance for Moroccan Dialect than classical approaches and we achieved an accuracy of 90%.Comment: 13 pages, 5 tables, 2 figure

    latent Dirichlet allocation method-based nowcasting approach for prediction of silver price

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    Silver is a metal that offers significant value to both investors and companies. The purpose of this study is to make an estimation of the price of silver. While making this estimation, it is planned to include the frequency of searches on Google Trends for the words that affect the silver price. Thus, it is aimed to obtain a more accurate estimate. First, using the Latent Dirichlet Allocation method, the keywords to be analyzed in Google Trends were collected from various articles on the Internet. Mining data from Google Trends combined with the information obtained by LDA is the new approach this study took, to predict the price of silver. No study has been found in the literature that has adopted this approach to estimate the price of silver. The estimation was carried out with Random Forest Regression, Gaussian Process Regression, Support Vector Machine, Regression Trees and Artificial Neural Networks methods. In addition, ARIMA, which is one of the traditional methods that is widely used in time series analysis, was also used to benchmark the accuracy of the methodology. The best MSE ratio was obtained as 0,000227131 ± 0.0000235205 by the Regression Trees method. This score indicates that it would be a valid technique to estimate the price of "Silver" by using Google Trends data using the LDA method

    The development of the Kent coalfield 1896-1946

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    One of the unique features of the Kent Coalfield is that it is entirely concealed by newer rocks. The existence of a coalfield under southern England, being a direct link between those of South Wales, Somerset and Bristol in the west and the Ruhr, Belgium. and northern France in the east, was predicted by the geologist R. A. C. Godwin-Austen as early as 1856. It was, however, only the rapid increase in demand for Britain's coal in the last quarter of the nineteenth century that made it worth considering testing this hypothesis. The first boring was made in the years 1886-90, and although it discovered coal, this did not in itself prove the existence of a viable coalfield. This could be done only by incurring the heavy cost of boring systematically over a wide area. As the financial returns from such an undertaking were uncertain, it was not surprising that in the early years, around the turn of the century, a dominant role was played by speculators, who were able to induce numerous small investors to risk some of their savings in the expectation of high profits. As minerals in Britain were privately owned, the early pioneer companies not only had to meet the cost of the exploratory borines, but also, if they were not to see the benefit of their work accrue to others, lease beforehand the right to mine coal from local landowners in as much of the surrounding area as possible. This policy was pursued most vigorously by Arthur Burr, a Surrey land specula tor, who raised capital by creating the Kent Coal Conoessions Ltd. and then floating a series of companies allied to it. Burr's enterprise would probably have been. successful had it not been for the water problems encountered at depth in -v- the coalfield. As a result, the Concessions group found itself in control of most of the coalfield, but without the necessary capital to sink and adequately equip its 01ffi collieries. By 1910, however, the discovery of iron ore deposits in east Kent, coupled with the fact that Kent coal was excellent for coking purposes, began to attract the large steel firms of Bolckow, Vaughan Ltd. and Dorman, Long & Co. Ltd. in to the area. The First World War intervened, however, to delay their plans, and to provide an extended lease of life to the Concessions group, which, by the summer of 1914, was facing financial collapse. By the time Dorman, Lone & Co, in alliance with Weetman Pearson (Lord Cowdray), had acquired control over the greater part of the coalfield from the Concessions group, not only was the country's coal industry declining, but so was its steel industry, which suffered an even more severe rate of contraction during the inter-war years. As a result, Pearson and Dorman Long Ltd. was forced to concentrate just on coal production, and this in turn was hampered not only by the water problems, but also by labour shortages and the schemes introduced by the government in 1930 to restrict the country's coal output, in an attempt to maintain prices and revenue in the industry. Nevertheless, production did show a substantial increase between 1927 and 1935, after which it declined as miners left the coalfield to return to their former districts, where employment opportunities were improving in the late thirties. Supporting roles were played in the inter-war years by Richard Tilden Smith, a share underwriter turned industrialist with long standing interests in the coalfield, who acquired one of the Concessions group's two collieries, and by the Powell Duffryn Steam Coal Co. Ltd., which through subsidiary companies, took over the only colliery to be developed by a pioneer company outside the Concessions group. The impossibility of Kent coal, because of its nature, ever gaining more than token access to the more lucrative household market, and then the failure of the local steel industry to materialise meant that the -vi- companies had to develop alternative outlets for their growing outputs. Although nearness to industrial markets in the south-east of England did confer certain advantages were poor consolation for the hoped for developments of either the early pioneers or the later industrialists. Instead of the expected profits, the companies mostly incurred losses, and only the company acquired by Powell Duffryn ever paid a dividend to its shareholders in the years before nationalisation. From the point of view of the Kent miners, the shortage of labour in the coalfield, particularly in the years 1914-20 and 1927-35, was to an important extent responsible for their being amongst the highest paid in the industry. At the same time the more favourable employment opportunities prevailing in Kent compared with other mining districts enabled the Kent Nine Workers Association to develop into a well organised union, which on the whole was able to look after the interests of its members fairly successfully. Throughout the period 1896 to 1946 the Kent Coalfield existed very much at the margin of the British coal industry. Its failure to develop substantially along the lines envisaged by either the early pioneers or by the later industrialists meant that its importance in national terms always remained small

    Gamification in E-Learning: game factors to strengthen specific English pronunciation features in undergraduate students at UPTC Sogamoso

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    Appendix A Characterization survey (104), Appendix B. EFL Students’ questionnaire (109), Appendix C. Characterization survey: data treatment question (113), Appendix D. Informed consent letter, English version (114), Appendix E. Carta de consentimiento informado, versión en español (117), Appendix F. Time Schedule (120), Appendix G. Sample Challenges at Moodle (126), Appendix H. Participants’ questionnaire results (128).La gamificación es un término que suele denotar el uso de componentes del juego en situaciones no relacionadas con el juego en sí para crear experiencias de aprendizaje agradables, divertidas y motivadoras para los estudiantes (Werbach y Hunter, 2012). Por lo tanto, el análisis de los factores básicos de los juegos se convierte en algo esencial a la hora de definir y utilizar la gamificación como estrategia de mediación del inglés como lengua extranjera para fortalecer rasgos específicos de pronunciación en los estudiantes de pregrado de la UPTC Sogamoso. El procedimiento de estudio se basa en la investigación acción mediante la implementación de la estrategia de gamificación para la mediación en la pronunciación del inglés, orientada a treinta estudiantes de diferentes programas de ingeniería, administración y tecnología con niveles heterogéneos de dominio del inglés. Las actividades se centran principalmente en la producción de sonidos, el ritmo, el acento y la entonación, los rasgos de pronunciación segmental y suprasegmental. Los resultados arrojaron una evidente mejora en las características segméntales y suprasegmentales de la percepción en la pronunciación de los participantes así como la contribución del objetivo de los juegos a la instrucción fonética y fonológica, la sensación en el juego a la motivación para mejorar la pronunciación, el reto establecido en los juegos a la actitud positiva de los participantes, y la sociabilidad a la exposición practica de la pronunciación inglesa.Gamification is a relatively new term that often denotes the use of game components in situations unrelated to the game itself to create enjoyable, fun, and motivating learning experiences for students (Werbach and Hunter, 2012). Therefore, analyzing the games' basic factors becomes essential when defining and using gamification as a strategy for English as Foreign Language mediation to strengthen specific pronunciation features in UPTC Sogamoso undergraduate students. The study procedure is based on action research by implementing the gamification strategy for mediation in English pronunciation, oriented to thirty students from different engineering, management, and technology programs at heterogeneous levels of English proficiency. The activities mainly focus on sound production, rhythm, stress, and intonation, segmental and suprasegmental pronunciation features. The results showed an evident improvement in the segmental and suprasegmental features of the participants' pronunciation perception as well as the contribution of game goals to phonetics and phonological instruction, the game sensation to the motivation for pronunciation improvement, the game challenge to the participants' positive attitude, and the sociality to the English pronunciation exposure practice
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