4,634 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Gabriel Harvey and the History of Reading: Essays by Lisa Jardine and others

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    Few articles in the humanities have had the impact of Lisa Jardine and Anthony Grafton’s seminal ‘Studied for Action’ (1990), a study of the reading practices of Elizabethan polymath and prolific annotator Gabriel Harvey. Their excavation of the setting, methods and ambitions of Harvey’s encounters with his books ignited the History of Reading, an interdisciplinary field which quickly became one of the most exciting corners of the scholarly cosmos. A generation inspired by the model of Harvey fanned out across the world’s libraries and archives, seeking to reveal the many creative, unexpected and curious ways that individuals throughout history responded to texts, and how these interpretations in turn illuminate past worlds. Three decades on, Harvey’s example and Jardine’s work remain central to cutting-edge scholarship in the History of Reading. By uniting ‘Studied for Action’ with published and unpublished studies on Harvey by Jardine, Grafton and the scholars they have influenced, this collection provides a unique lens on the place of marginalia in textual, intellectual and cultural history. The chapters capture subsequent work on Harvey and map the fields opened by Jardine and Grafton’s original article, collectively offering a posthumous tribute to Lisa Jardine and an authoritative overview of the History of Reading

    Configuration Management of Distributed Systems over Unreliable and Hostile Networks

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    Economic incentives of large criminal profits and the threat of legal consequences have pushed criminals to continuously improve their malware, especially command and control channels. This thesis applied concepts from successful malware command and control to explore the survivability and resilience of benign configuration management systems. This work expands on existing stage models of malware life cycle to contribute a new model for identifying malware concepts applicable to benign configuration management. The Hidden Master architecture is a contribution to master-agent network communication. In the Hidden Master architecture, communication between master and agent is asynchronous and can operate trough intermediate nodes. This protects the master secret key, which gives full control of all computers participating in configuration management. Multiple improvements to idempotent configuration were proposed, including the definition of the minimal base resource dependency model, simplified resource revalidation and the use of imperative general purpose language for defining idempotent configuration. Following the constructive research approach, the improvements to configuration management were designed into two prototypes. This allowed validation in laboratory testing, in two case studies and in expert interviews. In laboratory testing, the Hidden Master prototype was more resilient than leading configuration management tools in high load and low memory conditions, and against packet loss and corruption. Only the research prototype was adaptable to a network without stable topology due to the asynchronous nature of the Hidden Master architecture. The main case study used the research prototype in a complex environment to deploy a multi-room, authenticated audiovisual system for a client of an organization deploying the configuration. The case studies indicated that imperative general purpose language can be used for idempotent configuration in real life, for defining new configurations in unexpected situations using the base resources, and abstracting those using standard language features; and that such a system seems easy to learn. Potential business benefits were identified and evaluated using individual semistructured expert interviews. Respondents agreed that the models and the Hidden Master architecture could reduce costs and risks, improve developer productivity and allow faster time-to-market. Protection of master secret keys and the reduced need for incident response were seen as key drivers for improved security. Low-cost geographic scaling and leveraging file serving capabilities of commodity servers were seen to improve scaling and resiliency. Respondents identified jurisdictional legal limitations to encryption and requirements for cloud operator auditing as factors potentially limiting the full use of some concepts

    Graduate Catalog of Studies, 2023-2024

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    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Predicting Paid Certification in Massive Open Online Courses

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    Massive open online courses (MOOCs) have been proliferating because of the free or low-cost offering of content for learners, attracting the attention of many stakeholders across the entire educational landscape. Since 2012, coined as “the Year of the MOOCs”, several platforms have gathered millions of learners in just a decade. Nevertheless, the certification rate of both free and paid courses has been low, and only about 4.5–13% and 1–3%, respectively, of the total number of enrolled learners obtain a certificate at the end of their courses. Still, most research concentrates on completion, ignoring the certification problem, and especially its financial aspects. Thus, the research described in the present thesis aimed to investigate paid certification in MOOCs, for the first time, in a comprehensive way, and as early as the first week of the course, by exploring its various levels. First, the latent correlation between learner activities and their paid certification decisions was examined by (1) statistically comparing the activities of non-paying learners with course purchasers and (2) predicting paid certification using different machine learning (ML) techniques. Our temporal (weekly) analysis showed statistical significance at various levels when comparing the activities of non-paying learners with those of the certificate purchasers across the five courses analysed. Furthermore, we used the learner’s activities (number of step accesses, attempts, correct and wrong answers, and time spent on learning steps) to build our paid certification predictor, which achieved promising balanced accuracies (BAs), ranging from 0.77 to 0.95. Having employed simple predictions based on a few clickstream variables, we then analysed more in-depth what other information can be extracted from MOOC interaction (namely discussion forums) for paid certification prediction. However, to better explore the learners’ discussion forums, we built, as an original contribution, MOOCSent, a cross- platform review-based sentiment classifier, using over 1.2 million MOOC sentiment-labelled reviews. MOOCSent addresses various limitations of the current sentiment classifiers including (1) using one single source of data (previous literature on sentiment classification in MOOCs was based on single platforms only, and hence less generalisable, with relatively low number of instances compared to our obtained dataset;) (2) lower model outputs, where most of the current models are based on 2-polar iii iv classifier (positive or negative only); (3) disregarding important sentiment indicators, such as emojis and emoticons, during text embedding; and (4) reporting average performance metrics only, preventing the evaluation of model performance at the level of class (sentiment). Finally, and with the help of MOOCSent, we used the learners’ discussion forums to predict paid certification after annotating learners’ comments and replies with the sentiment using MOOCSent. This multi-input model contains raw data (learner textual inputs), sentiment classification generated by MOOCSent, computed features (number of likes received for each textual input), and several features extracted from the texts (character counts, word counts, and part of speech (POS) tags for each textual instance). This experiment adopted various deep predictive approaches – specifically that allow multi-input architecture - to early (i.e., weekly) investigate if data obtained from MOOC learners’ interaction in discussion forums can predict learners’ purchase decisions (certification). Considering the staggeringly low rate of paid certification in MOOCs, this present thesis contributes to the knowledge and field of MOOC learner analytics with predicting paid certification, for the first time, at such a comprehensive (with data from over 200 thousand learners from 5 different discipline courses), actionable (analysing learners decision from the first week of the course) and longitudinal (with 23 runs from 2013 to 2017) scale. The present thesis contributes with (1) investigating various conventional and deep ML approaches for predicting paid certification in MOOCs using learner clickstreams (Chapter 5) and course discussion forums (Chapter 7), (2) building the largest MOOC sentiment classifier (MOOCSent) based on learners’ reviews of the courses from the leading MOOC platforms, namely Coursera, FutureLearn and Udemy, and handles emojis and emoticons using dedicated lexicons that contain over three thousand corresponding explanatory words/phrases, (3) proposing and developing, for the first time, multi-input model for predicting certification based on the data from discussion forums which synchronously processes the textual (comments and replies) and numerical (number of likes posted and received, sentiments) data from the forums, adapting the suitable classifier for each type of data as explained in detail in Chapter 7

    3D Innovations in Personalized Surgery

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    Current practice involves the use of 3D surgical planning and patient-specific solutions in multiple surgical areas of expertise. Patient-specific solutions have been endorsed for several years in numerous publications due to their associated benefits around accuracy, safety, and predictability of surgical outcome. The basis of 3D surgical planning is the use of high-quality medical images (e.g., CT, MRI, or PET-scans). The translation from 3D digital planning toward surgical applications was developed hand in hand with a rise in 3D printing applications of multiple biocompatible materials. These technical aspects of medical care require engineers’ or technical physicians’ expertise for optimal safe and effective implementation in daily clinical routines.The aim and scope of this Special Issue is high-tech solutions in personalized surgery, based on 3D technology and, more specifically, bone-related surgery. Full-papers or highly innovative technical notes or (systematic) reviews that relate to innovative personalized surgery are invited. This can include optimization of imaging for 3D VSP, optimization of 3D VSP workflow and its translation toward the surgical procedure, or optimization of personalized implants or devices in relation to bone surgery

    A BIM - GIS Integrated Information Model Using Semantic Web and RDF Graph Databases

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    In recent years, 3D virtual indoor and outdoor urban modelling has become an essential geospatial information framework for civil and engineering applications such as emergency response, evacuation planning, and facility management. Building multi-sourced and multi-scale 3D urban models are in high demand among architects, engineers, and construction professionals to achieve these tasks and provide relevant information to decision support systems. Spatial modelling technologies such as Building Information Modelling (BIM) and Geographical Information Systems (GIS) are frequently used to meet such high demands. However, sharing data and information between these two domains is still challenging. At the same time, the semantic or syntactic strategies for inter-communication between BIM and GIS do not fully provide rich semantic and geometric information exchange of BIM into GIS or vice-versa. This research study proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graph databases. The suggested solution's originality and novelty come from combining the advantages of integrating BIM and GIS models into a semantically unified data model using a semantic framework and ontology engineering approaches. The new model will be named Integrated Geospatial Information Model (IGIM). It is constructed through three stages. The first stage requires BIMRDF and GISRDF graphs generation from BIM and GIS datasets. Then graph integration from BIM and GIS semantic models creates IGIMRDF. Lastly, the information from IGIMRDF unified graph is filtered using a graph query language and graph data analytics tools. The linkage between BIMRDF and GISRDF is completed through SPARQL endpoints defined by queries using elements and entity classes with similar or complementary information from properties, relationships, and geometries from an ontology-matching process during model construction. The resulting model (or sub-model) can be managed in a graph database system and used in the backend as a data-tier serving web services feeding a front-tier domain-oriented application. A case study was designed, developed, and tested using the semantic integrated information model for validating the newly proposed solution, architecture, and performance
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