162 research outputs found
PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers
No full textWith the rapid growth of scholarly archives, researchers subscribe to “paper alert” systems that periodically provide them with recommendations of recently published papers that are similar to previously collected papers. However, researchers sometimes struggle to make sense of nuanced connections between recommended papers and their own research context, as existing systems only present paper titles and abstracts. To help researchers spot these connections, we present PaperWeaver, an enriched paper alerts system that provides contextualized text descriptions of recommended papers based on user-collected papers. PaperWeaver employs a computational method based on Large Language Models (LLMs) to infer users' research interests from their collected papers, extract context-specific aspects of papers, and compare recommended and collected papers on these aspects. Our user study (N=15) showed that participants using PaperWeaver were able to better understand the relevance of recommended papers and triage them more confidently when compared to a baseline that presented the related work sections from recommended papers
The Business Intelligence as a Service in the Cloud
No full textLimitations imposed by the traditional practice in financial institutions of running risk analysis on the desktop mean many rely on models which assume a “normal” Gaussian distribution of events which can seriously underestimate the real risk. In this paper, we propose an alternative service which uses the elastic capacities of Cloud Computing to escape the limitations of the desktop and produce accurate results more rapidly.The Business Intelligence as a Service (BIaaS) in the Cloud has a dual-service approach to compute risk and pricing for financial analysis. The first type of BIaaS service uses three APIs to simulate the Heston Model to compute the risks and asset prices, and computes the volatility (unsystematic risks) and the implied volatility (systematic risks) which can be tracked down at any time. The second type of BIaaS service uses two APIs to provide business analytics for stock market analysis, and compute results in the visualised format, so that stake holders without prior knowledge can understand. A full case study with two sets of experiments is presented to support the validity and originality of BIaaS. Additional three examples are used to support accuracy of the predicted stock index movement as a result of the use of the Heston Model and its associated APIs.We describe the architecture of deployment, together with examples and results which show how our approach improves risk and investment analysis and maintaining accuracy and efficiency whilst improving performance over desktops
Smart learning futures: a report from the 3rd US-China smart education conference
No full textThe third day of the third US-China Smart Education Conference featured a discussion with 27 thinkers representing higher education, business and industry, vocational training, and policy making. Researchers from the fields of artificial intelligence, computer science, educational technology, neuro-science and the learning sciences from many countries actively participated and are collectively the co-authors of this report. After two short presentations on computational neuropsychology and the next generation of artificial intelligence by two of the participants (Thomas Parsons and Yang Yang respectively), the participants were divided into four groups led by Phillip Harris (AECT Director), Joseph South (ISTE Chief Learning Officer), Chee-Kit Looi (NIE Head of the Learning Sciences Lab), and Maiga Chang (School of Computing and Information Systems, Athabasca University). The groups were asked to consider the following four questions: (a) What are the 5 most promising technologies likely to transform education in the next 10 years? (b) How do/will advanced learning technologies impact the future of education? (c) What challenges do advanced learning technologies bring to education? (d) What are the new demands for education in the future of society? The groups could focus as they deemed appropriate, modifying adding questions or ignoring any question. This report is a synthesis of those discussions
Financial software as a service – a paradigm for risk modelling and analytics
No full textSoftware as a service as one of the cloud delivery models that supports fine-grained components. Financial applications demand better performance and accuracy in a cloud than the traditional computing platforms. Therefore, designing financial software as a service (FSaaS) requires an engineering and systematic approach. This paper has proposed an integrated service-oriented architecture and a SaaS component model for financial domain that provides the required scalability, flexibility and customisation. We have also demonstrated the design and customisation of service component interfaces to a financial simulation so that it provides automatic prediction models for investors to know accurate results for buy and sale prices. Therefore, large-scaled simulations can be achieved within a matter of 13.5 second for outlier removal and within 9 seconds for high-performance risk computation on the Cloud. We show the holistic and complete approach of illustrating the system design of FSaaS, showing the two major algorithms and the results of experiments of running these two algorithms. We provide plans to integrate new and existing services with FSaaS
Business integration as a service: computational risk analysis for small and medium enterprises adopting SAP
No full textFinancial Clouds and modelling offered by Cloud Computing Adoption Framework
No full textCloud Computing Adoption Framework (CCAF) is a framework for designing and implementation of Could Computing solutions. This paper focuses on how CCAF can help to address portability in Cloud Computing implementations in Finance domain. Portability involves migrating entire applications from desktops to clouds and between different Clouds in a way which is transparent to users so they may continue to work as if still using their familiar systems. Reviews for several financial models are studied, where Monte Carlo Methods (MCM) and Black Scholes Model (BSM) are chosen to demonstrate portability between desktops and clouds. A special technique in MCM, Variance-Gamma Process, is used for error corrections while performing analysis of good quality. Coding algorithm for MCM and BSM written in MATLAB are explained. Simulations for MCM and BSM are performed on different types of Clouds. Benchmark and experimental results are presented and discussed, together with implications for banking and ways to track risks in order to improve accuracy. We have used a conceptual Financial Cloud platform to explain how this fits into the CCAF, as well as Financial Software as a Service (FSaaS). Our objective is to demonstrate portability, speed, accuracy and reliability of applications in the clouds, while demonstrating portability for CCAF and FSaaS
ARXIVDIGESTABLES: Synthesizing Scientific Literature into Tables using Language Models
No full textWhen conducting literature reviews, scientists often create literature review tables-tables whose rows are publications and whose columns constitute a schema, a set of aspects used to compare and contrast the papers. Can we automatically generate these tables using language models (LMs)? In this work, we introduce a framework that leverages LMs to perform this task by decomposing it into separate schema and value generation steps. To enable experimentation, we address two main challenges: First, we overcome a lack of high-quality datasets to benchmark table generation by curating and releasing ARXIVDIGESTABLES, a new dataset of 2,228 literature review tables extracted from ArXiv papers that synthesize a total of 7,542 research papers. Second, to support scalable evaluation of model generations against human-authored reference tables, we develop DECONTEXTEVAL, an automatic evaluation method that aligns elements of tables with the same underlying aspects despite differing surface forms. Given these tools, we evaluate LMs' abilities to reconstruct reference tables, finding this task benefits from additional context to ground the generation (e.g. table captions, in-text references). Finally, through a human evaluation study we find that even when LMs fail to fully reconstruct a reference table, their generated novel aspects can still be useful
ComLittee: Literature Discovery with Personal Elected Author Committees
No full textIn order to help scholars understand and follow a research topic, significant
research has been devoted to creating systems that help scholars discover
relevant papers and authors. Recent approaches have shown the usefulness of
highlighting relevant authors while scholars engage in paper discovery.
However, these systems do not capture and utilize users' evolving knowledge of
authors. We reflect on the design space and introduce ComLittee, a literature
discovery system that supports author-centric exploration. In contrast to
paper-centric interaction in prior systems, ComLittee's author-centric
interaction supports curation of research threads from individual authors,
finding new authors and papers with combined signals from a paper recommender
and the curated authors' authorship graphs, and understanding them in the
context of those signals. In a within-subjects experiment that compares to an
author-highlighting approach, we demonstrate how ComLittee leads to a higher
efficiency, quality, and novelty in author discovery that also improves paper
discovery
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Hong Kong: workfare in the world's freest economy
No full textWorkfare was introduced in many countries to suppress welfare dependency and reduce social security expenditures. However, workfare was launched in Hong Kong when there was only a relatively small social security budget and its citizens still strongly adhered to the ideologies of self-reliance. It was found that workfare has performed several functions in Hong Kong. Firstly, it has forced unemployed claimants to give up benefits so that Hong Kong's social security expenditures can be saved. Secondly, workfare had combined with Hong Kong's semi-democratic polity so that extremely stigmatising welfare measures were implemented. Thirdly, it has pushed poor citizens to the labour market without having any protection over wages and working hours. Thus, the combination of workfare and a semi-democratic polity has successfully suppressed Hong Kong's welfare demands and strengthened its self-help spirit. As a result, Hong Kong's minimal social security scheme and its low tax policy have been maintained
A proposed model to analyse risk and return for a large computing system adoption
No full textThis thesis presents Organisational Sustainability Modelling (OSM), a new method to model and analyse risk and return systematically for the adoption of large systems such as Cloud Computing. Return includes improvements in technical efficiency, profitability and service. Risk includes controlled risk (risk-control rate) and uncontrolled risk (beta), although uncontrolled risk cannot be evaluated directly. Three OSM metrics, actual return value, expected return value and risk-control rate are used to calculate uncontrolled risk. The OSM data collection process in which hundreds of datasets (rows of data containing three OSM metrics in each row) are used as inputs is explained. Outputs including standard error, mean squared error, Durbin-Watson, p-value and R-squared value are calculated. Visualisation is used to illustrate quality and accuracy of data analysis. The metrics, process and interpretation of data analysis is presented and the rationale is explained in the review of the OSM method.Three case studies are used to illustrate the validity of OSM:• National Health Service (NHS) is a technical application concerned with backing up data files and focuses on improvement in efficiency.• Vodafone/Apple is a cost application and focuses on profitability.• The iSolutions Group, University of Southampton focuses on service improvement using user feedback.The NHS case study is explained in detail. The expected execution time calculated by OSM to complete all backup activity in Cloud-based systems matches actual execution time to within 0.01%. The Cloud system shows improved efficiency in both sets of comparisons. All three case studies confirm there are benefits for the adoption of a large computer system such as the Cloud. Together these demonstrations answer the two research questions for this thesis:1. How do you model and analyse risk and return on adoption of large computing systems systematically and coherently?2. Can the same method be used in risk mitigation of system adoption?Limitations of this study, a reproducibility case, comparisons with similar approaches, research contributions and future work are also presented
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