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12th International Semantic Web Conference, ISWC 2013

作者： Dashdorj, Zolzaya Serafini, Luciano Semantic and Knowledge Innovation Lab. Telecom Italia Italy Department of Information Engineering and Computer Science University of Trento Italy Data and Knowledge Management Unit Fondazione Bruno Kessler Italy

The increasing availability of massive mobile phone call data records (CDR) has opened new opportunities for analyzing and understanding real-life social phenomena and human dynamics. In order to better interpret this enormous amount of data it is useful to contextualize them with information about the circumstances under which they has been generated. Nowadays, linked open data initiative provide access to a huge amount of geo-time referenced knowledge about territory and events that happen in the territory. These informations can be used to characterize the aforementioned context. The aim of this Ph.D is to investigate on the intercorrelations between CDR, contexts, and human behaviors. The ultimate goal is to build a stochastic model, that can be used to predict semantic (qualitative) behavioral patterns on the basis of CDR traffic and context and identify and explain anomalous situations on the basis of deviations from standard CDR patterns.

关键词： knowledge management

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Principles for Designing Activity-based Personalized eLearning

Principles for Designing Activity-based Personalized eLearni...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Owen Conlan Ian O'Keeffe Aoife Brady Vincent Wade Knowledge and Data Engineering Group Department of Computer Science Trinity College Dublin Ireland

Activity-based personalized eLearning involves delivering to a learner a learning experience that is tailored to their individual needs. Traditionally, these needs have been met through the personalized selection of content that fits the learner's preferences. However, by coupling this approach with the possibilities of interactive services a more engaging form of personalized education may be realized. This paper describes the design principles for producing activity-based personalized distance learning offerings.

关键词： Electronic learning Web services computer aided instruction Adaptive systems Artificial intelligence data engineering computer science Educational institutions Navigation Automated highways

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Dynamic ontology integration in a multi-agent environment

Dynamic ontology integration in a multi-agent environment

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International Conference on Advanced Information Networking and Applications (AINA)

作者： S.M. Deen K. Ponnamperuma Data and Knowledge Engineering DAKE Group Department of Computer Science University of Keele UK

We present here an approach for dynamic ontology integration for a multi-agent environment, in which each agent holds the ontologies of its acquaintances (i.e., other agents of its interest) as the integrated partial global ontology, which is essential to interpret the local schemas for inter-agent operations. This integration has to be carried out whenever a new acquaintance is added or when the local ontology of an acquaintance changes. The approach described is general (i.e., independent of any particular thesaurus) and it carries out the integration automatically except for minimal unavoidable human inputs to resolve semantic conflicts if discovered in the process.

关键词： Ontologies Thesauri Humans Semantic Web Peer to peer computing Multiagent systems knowledge engineering computer science Shape Scalability

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Local methods for estimating SimRank score

Local methods for estimating SimRank score

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12th International Asia Pacific Web Conference, APWeb 2010

作者： Jia, Xu Liu, Hongyan Zou, Li He, Jun Du, Xiaoyong Cai, Yuanzhe Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China Department of Computer Science Renmin University of China 100872 China Department of Management Science and Engineering Tsinghua University 100084 China Department of Computer Science University of Texas Arlington TX 76019 United States

ISBN: (纸本)9780769540122

SimRank is a well known algorithm which conducts link analysis to measure similarity between each pair of nodes (nodepair). But it suffers from high computational cost, limiting its usage in large-scale datasets. Moreover, Links between nodes are changing over time. It may be desirable to quickly approximate the similarity score between certain nodepair without performing a large-scale computation on the entire graph. In our approach we propose a method to efficiently estimate the similarity score using only a small subgraph of the entire graph. We call this novel algorithm "Local-SimRank". The experimental results conducted on real datasets and synthetic dataset show that our algorithm efficiently produces good approximations to the global SimRank scores. Meanwhile, we prove that the Local-SimRank score LS(a, b) is always less than original SimRank score S(a, b) mathematically. © 2010 IEEE.

关键词： Approximation algorithms

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Short Text Topic Modeling Techniques, Applications, and Performance: A Survey

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IEEE Transactions on knowledge and data engineering 2022年第3期34卷 1427-1445页

作者： Qiang, Jipeng Qian, Zhenyu Li, Yun Yuan, Yunhao Wu, Xindong Department of Computer Science Yangzhou University Jiangsu225009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230026 China Mininglamp Academy of Sciences Minininglamp Beijing China

Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is available in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm. © 2020 IEEE.

关键词： Surveys

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A Comparison of the Instructor-Trainee Dance dataset Using Cosine similarity, Euclidean distance, and Angular difference 26

A Comparison of the Instructor-Trainee Dance Dataset Using C...

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26th International computer science and engineering Conference, ICSEC 2022

作者： Srikaewsiew, Thanawat Khianchainat, Khatadet Tharatipyakul, Atima Pongnumkul, Suporn Kanjanawattana, Sarunya Institute of Engineering Suranaree University of Technology Department of Computer Engineering Nakhon Ratchasima Thailand Data Science and Analytics Research Group Pathumthani Thailand

ISBN: (纸本)9781665491983

The COVID-19 outbreak has restricted most outdoor activities, leads to increasing interest in exercise at home with online trainers. One issue of online exercise technology is the safety since improper motion might result in injury. As a basis to prevent improper motion, methods for evaluating the motion similarity between an instructor and a trainee are essential. Cosine similarity, Angular difference, and Euclidean distance are three general ways for the motion evaluation. This study aimed to determine the most effective way for analyzing the similarity of human motion on the dataset of instructor-led dances. We first experimented with the data to find the appropriate cut-off value for classifying posture into two classes based on the similarity score. Confusion matrix, precision, recall, F1-score, accuracy of the results were then used to compare the efficiency. We discovered that Cosine similarity had the highest accuracy, 82.77 percent at cut-off 93. © 2022 IEEE.

关键词： COVID-19

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Multilingual adaptive search for digital libraries

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Lecture Notes in computer science

作者： Ghorab, M. Rami Leveling, Johannes Lawless, Séamus O'Connor, Alexander Zhou, Dong Jones, Gareth J. F. Wade, Vincent Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland CNGL School of Computing Dublin City University Dublin 9 Ireland

ISBN: (纸本)9783642244681

We describe a framework for Adaptive Multilingual Information Retrieval (AMIR) which allows multilingual resource discovery and delivery using on-the-fly machine translation of documents and queries. Result documents are presented to the user in a contextualised manner. Challenges and affordances of both adaptive and multilingual IR, with a particular focus on digital libraries, are detailed. The framework components are motivated by a series of results from experiments on query logs and documents from The European Library. We conclude that factoring adaptivity and multilinguality aspects into the search process can enhance the user's experience with online digital libraries. © 2011 Springer-Verlag.

关键词： Digital libraries

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A brief overview of tools and evaluation methods

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012年 7250卷 287-300页

作者： Gossen, Tatiana Nitsche, Marcus Haun, Stefan Nürnberger, Andreas Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke-University Germany

ISBN: (纸本)9783642318290

In this chapter we explain the definition of the term (data) exploration. We refine this definition in the context of browsing, navigating and searching. We provide a definition of bisociative exploration and derive requirements on user interfaces, which are designed to support bisociative knowledge discovery. We discuss how to support subtasks of bisociative data exploration with appropriate user interface elements. We also present a set of exploratory tools, which are currently available or in development. Finally, we discuss the problem of usability evaluation in the context of exploratory search. Two main issues - complexity and comparability - are explained and possible solutions proposed. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： User interfaces

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Exploration: Overview

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012年 7250卷 285-286页

作者： Nürnberger, Andreas Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke-University Germany

ISBN: (纸本)9783642318290

In the previous chapters of this book quite different approaches to create networks based on existing data collections (Part II) have been discussed and diverse methods for network analysis have been proposed (Part III). All these methods provide powerful means in order to obtain different insights into the properties of huge information networks or graphs. However, one disadavantage of these individual approaches is that each approach provides only specific facets of information to the end user. Integrated exploration tools that enable the interactive exploration of huge graphs and data collections using data viusalization, aggregation and mining methods could be much more beneficial for the (interactive) task of finding bisociations. Therefore, we present and discuss in this part of the book methods for interactive exploration that bring these fields together. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： data acquisition

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Improving Zero-Shot Semantic Segmentation using Dynamic Kernels

Improving Zero-Shot Semantic Segmentation using Dynamic Kern...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Tajwar, Tauseef Rahman, Muftiqur Chowdhury, Taukir Azam Ahmed, Sabbir Farazi, Moshiur Kabir, Md. Hasanul Islamic University of Technology Department of Computer Science and Engineering Gazipur1704 Bangladesh United International University Department of Computer Science and Engineering Dhaka Bangladesh Imaging and Computer Vision Group Data61-CSIRO Canberra Australia

ISBN: (纸本)9798350382204

Zero-shot Semantic Segmentation (ZS3) is a challenging task that segments objects belonging to classes that are completely unseen during training. An established and intuitive approach is to formulate ZS3 as a combination of two subtasks where, at first, mask proposals are generated and then each pixel in those regions is assigned a class label. Most of the existing works struggle to generate masks with high generalization capability, which results in significant underperformance in unseen classes. In this connection, we propose the use of 'Dynamic Kernels' to help a ZS3 model better 'understand' the objects in the training phase by taking advantage of their inherent inductive biases to generate better mask proposals. They act as specialized agents that are updated based on their corresponding contents from the seen classes and then utilize that knowledge to understand unseen objects. The proposed pipeline also leverages the Contrastive Language-Image Pre-Training (CLIP) architecture to perform segment classification which further improves the generalization performance by exploiting its cross-modal training. Dynamic kernels go hand-in-hand with CLIP since it is able to process the granularity of CLIP from image level to pixel level resulting in performance improvement for both the seen and unseen classes. Our method, 'Zero-Shot dynamic Kernel Network' (ZSK-Net), outperforms the previous works by achieving +6.4h IoU on the Pascal VOC dataset. It also achieves state-of-the-art result on the COCO-Stuff dataset by +0.9h IoU on a single prompt setting. © 2023 IEEE.

关键词： Semantic Segmentation

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