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2010 International Conference on Network and Service Management, CNSM 2010

作者： Feeney, Kevin Brennan, Rob Foley, Simon N. FAME Knowledge and Data Engineering Group Trinity College Dublin Ireland FAME Department of Computer Science University College Cork Ireland

ISBN: (纸本)9781424489084

Federated policy systems are required to support the complexity and organizational heterogeneity of the modern marketplace. The Community-based Policy Management System (CBPMS) is such a distributed policy management approach. It utilizes a tree-based capability authority model to partition and delegate federated capabilities. However CBPMS delegation chains have limitations such as: performance overheads due to distributed rule evaluation, threats from malformed or malicious federated principals and a lack of flexibility with respect to delegation chain reduction or capability authority re-partitioning. In this paper we introduce a trust management model for CBPMS that addresses all of these issues. A brief security analysis is presented and a telecommunications service management use case described. © 2010 IEEE.

关键词： Chains

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Resource-gathering algorithms in the game of starcraft

Resource-gathering algorithms in the game of starcraft

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2017 IEEE Conference on Computational Intelligence and Games, CIG 2017

作者： Rooijackers, Martin L.M. Winands, Mark H.M. Games AI Group Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (纸本)9781538632338

StarCraft is a real-time strategy game, which has a large state space, and commonly features two opposing players, capable of acting simultaneously. One of the aspects of the game is resource gathering. Each agent playing StarCraft has to gather minerals from nearby mineral field in order to produce more units. The more resources can be gathered, the larger the army is to attack the opponent and win the game. We present five algorithms that can be used for resource gathering for an intelligent agent playing the game of StarCraft: Brood War. The results reveal that improving the scheduling or the path finding improve the resource gathering considerably. © 2017 IEEE.

关键词： Intelligent agents

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Can museum exhibits support personalised learning in collaborative classroom activities by using augmented reality?

Can museum exhibits support personalised learning in collabo...

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IADIS International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2005

作者： Monthienvichienchai, Rachada Conlan, Owen Seyedarabi, Faezeh London Knowledge Lab. Institute of Education University of London United Kingdom Knowledge and Data Engineering Group Department of Computer Science Trinity College Dublin Ireland

ISBN: (纸本)9781627483308

This paper proposes a novel approach in integrating seemingly contradictory approaches of teaching and learning via collaborative and personalised learning activities by using augmented reality museum exhibits. The paper highlights the benefits that computer supported collaborative learning and computer supported personalised learning bring or promise to bring to the learner. A proposal is given for a study that will setup a common ground for these two approaches via a location-aware augmented reality museum display. The principal aim of the study is to investigate how the process of learning collaboratively is intertwined with personalised experience of visiting the museum display. Ultimately, the result of the study will provide a new understanding of visitor's experience of museum displays, not only within the context of the museum's location, but also within the classroom where the visit truly begins and finishes.

关键词： Augmented reality

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Discrepancies between reality and expectation: Can Adaptive Hypermedia meet the expectations of teachers?

Discrepancies between reality and expectation: Can Adaptive ...

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IADIS International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2005

ISBN: (纸本)9781627483308

The effort to personalise eLearning has led to the development of sophisticated Adaptive Hypermedia (AH) systems that can adapt to many aspects of the learner's characteristics or explicit preferences. However, while many of these adaptations are grounded in sound pedagogical theories, it is unclear how effective such adaptations are in supporting everyday teaching and learning processes that take place in the classroom today. This paper presents a study that explores what teachers in the UK, who have been named by their colleagues as implementing best-practice in the classroom, expect from an eLearning platform that can adapt to the individual needs of their students. The result of the study shows that there are currently some discrepancies between the teachers' expectations and what adaptive hypermedia currently offers. The paper then concludes by describing how these discrepancies can be addressed in future AH systems.

关键词： Students

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Ontology mapping representations: A pragmatic evaluation

Ontology mapping representations: A pragmatic evaluation

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作者： Thomas, Hendrik O'Sullivan, Declan Brennan, Rob Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland

ISBN: (纸本)1891706241

A common approach to mitigate the effects of ontology heterogeneity is to discover and express the specific correspondences (mappings) between different ontologies. An open research question is: how should such ontology mappings be represented? In recent years several proposals for an ontology mapping representation have been published, but as yet no format is officially standardised or generally accepted in the community. In this paper we will present the results of a systematic analysis of ontology mapping representations to provide a pragmatic state of the art overview of their characteristics. In particular we are interested how current ontology mapping representations can support the management of ontology mappings (sharing, re-use, alteration) as well as how suitable they are for different mapping tasks.

关键词： Mapping

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Building linked open university data: tsinghua university open data as a showcase

Building linked open university data: tsinghua university op...

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Joint International Semantic Technology Conference, JIST 2011

作者： Ma, Yuanchao Xu, Bin Bai, Yin Li, Zonghui Knowledge Engineering Group Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783642299223

Linked Open University data applies semantic web and linked data technology to university data scenario, aiming at building interlinked semantic data around university information, providing possibility for unified inner- and inter- school information query and comparison. This paper proposes a general process of building linked open university data, with procedures covering choosing datasets and vocabularies, collecting and processing data, building RDF and interlink, etc. Tsinghua University Open data is used to demonstrate the process. Tsinghua University consist of 5 well-formed, interconnected datasets, with a number of interesting applications has been built on top of them. Finally, remarkable points about data collecting and processing is discussed. © 2012 Springer-Verlag.

关键词： Linked data

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A tag recommendation system based on contents

A tag recommendation system based on contents

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International Workshop on Discovery Challenge, DC 2009 at the European Conference on Machine Learning and Principles and Practice of knowledge Discovery in databases, ECML PKDD 2009

作者： Zhang, Ning Zhang, Yuan Tang, Jie Knowledge Engineering Group Department of Computer Science and Technology Tsinghua University Beijing China

Social bookmarking tools become more and more popular nowadays and tagging is used to organize information and allow users to recall or search the resources. Users need to type the tags whenever they post a resource, so that a good tag recommendation system can ease the process of finding some useful and relevant keywords for users. Researchers have made lots of relevant work for recommendation system, but those traditional collaborative systems do not fit to our tag recommendation. In this paper, we present two different methods: a simple language model and an adaption of topic model. We evaluate and compare these two approaches and show that a combination of these two methods will perform better results for the task one of PKDD Challenge 2009.

关键词： Recommender systems

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A collaborative filtering tag recommendation system based on graph

A collaborative filtering tag recommendation system based on...

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International Workshop on Discovery Challenge, DC 2009 at the European Conference on Machine Learning and Principles and Practice of knowledge Discovery in databases, ECML PKDD 2009

作者： Zhang, Yuan Zhang, Ning Tang, Jie Knowledge Engineering Group Department of Computer Science and Technology Tsinghua University Beijing China

With the rapid development of web2.0 technologies, tagging become much more important today to organize information and help users search the information they need with social bookmarking tools. In order to finish the second task of ECML PKDD challenge 2009, we propose a graph-based collaborative filtering tag recommendation system. We also refer to an algorithm called FolkRank, which is an adaptation of the famous Page Rank. We evaluate and compare these two approaches and show that a combination of these two methods will perform better results for our task.

关键词： Collaborative filtering

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8th Asia-Pacific Web Conference, APWeb 2006: Frontiers of WWW Research and Development

作者： Liang, Bangyong Tang, Jie Li, Juanzi Wang, Kehong Knowledge Engineering Group Department of Computer Science Tsinghua University 100084 Beijing China

ISBN: (纸本)3540311424

This paper addresses the issue of ontology caching on semantic web. The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. Ontology serves as the metadata for defining the information on semantic web. Ontology based semantic information retrieval (semantic retrieval) is becoming more and more important. Many research and industrial works have been made so far on semantic retrieval. Ontology based retrieval improves the performance of search engine and web mining. In semantic retrieval, a great number of accesses to ontologies usually lead the ontology servers to be very low efficient. To address this problem, it is indeed necessary to cache concepts and instances when ontology server is running. Existing caching methods from database community can be used in the ontology cache. However, they are not sufficient for dealing with the problem. In the task of caching in database, usually the most frequently accessed data are cached and the recently less frequently accessed data in the cache are removed from it. Different from that, in ontology base, data are organized as objects and relations between objects. User may request one object, and then request another object according to a relation of that object. He may also possibly request a similar object that has not any relations to the object. Ontology caching should consider more factors and is more difficult. In this paper, ontology caching is formalized as a problem of classification. In this way, ontology caching becomes independent from any specific semantic web application. An approach is proposed by using machine learning methods. When an object (e.g. concept or instance) is requested, we view its similar objects as candidates. A classification model is then used to predict whether each of these candidates should be cached or not. Features in classification models are defined. Experimental results indicat

关键词： data structures

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