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Design metamodels for domain-specific modelling methods using conceptual structures 11

Design metamodels for domain-specific modelling methods usin...

2018 Doctoral Consortium Papers presented at the 11th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modelling, PoEM-DC 2018

作者： Utz, Wilfrid Department of Knowledge Engineering Faculty of Computer Science University of Vienna Währingerstr. 29 Vienna1090 Austria

Enterprises nowadays operate in fast-changing environments and need to adapt dynamically to new circumstances. This impacts the way how enterprise information systems are analysed, designed and implemented. Conceptual modelling methods experience a constant evolution nowadays. The methods are re-constructed continuously to reflect the changing application/industrial domain. It is therefore required to provide additional design support to realise domain specific modelling methods and more precisely their underlying metamodel. The goal of this research project is to enable the knowledge representation of a metamodel to support its design process. Building upon the assumption that conceptual structures within a metamodel exist, a knowledge representation framework is proposed using conceptual graphs as the mathematical baseline. The proposal will be evaluated in a laboratory setting, applied on metamodel design challenges observed. Copyright 2018 for this paper by its authors.

关键词： knowledge representation

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Deep Bayesian optimization on attributed graphs

arXiv

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arXiv 2019年

作者： Cui, Jiaxu Yang, Bo Hu, Xia College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China Department of Computer Science and Engineering Texas A&M University College Station United States

Attributed graphs, which contain rich contextual features beyond just network structure, are ubiquitous and have been observed to benefit various network analytics applications. Graph structure optimization, aiming to find the optimal graphs in terms of some specific measures, has become an effective computational tool in complex network analysis. However, traditional model-free methods suffer from the expensive computational cost of evaluating graphs;existing vectorial Bayesian optimization methods cannot be directly applied to attributed graphs and have the scalability issue due to the use of Gaussian processes (GPs). To bridge the gap, in this paper, we propose a novel scalable Deep Graph Bayesian Optimization (DGBO) method on attributed graphs. The proposed DGBO prevents the cubical complexity of the GPs by adopting a deep graph neural network to surrogate black-box functions, and can scale linearly with the number of observations. Intensive experiments are conducted on both artificial and real-world problems, including molecular discovery and urban road network design, and demonstrate the effectiveness of the DGBO compared with the state-of-the-art. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

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PrCP: Pre-recommendation counter-polarization 10

PrCP: Pre-recommendation counter-polarization

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10th International Joint Conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2018

作者： Badami, Mahsa Nasraoui, Olfa Shafto, Patrick Knowledge Discovery and Web Mining Lab Computer Science and Computer Engineering Department University of Louisville LouisvilleKY United States Department of Mathematics and Computer Science Rutgers University - Newark NewarkNJ United States

ISBN: (纸本)9789897583308

Personalized recommender systems are commonly used to filter information in social media, and recommendations are derived by training machine learning algorithms on these data. It is thus important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We investigate how filtering and discovering information are affected by using recommender systems. We study the phenomenon of polarization within the context of the users interactions with a space of items and how this affects recommender systems. We then investigate the behavior of machine learning algorithms in such environments. Finally we propose a new recommendation model based on Matrix Factorization for existing collaborative filtering recommender systems in order to combating over-specialization in polarized environments toward counteracting polarization in human-generated data and machine learning algorithms. Copyright © 2018 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Polarization

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Estimating summary quality with pairwise preferences

Estimating summary quality with pairwise preferences

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2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018

作者： Zopf, Markus Research Training Group AIPHES Knowledge Engineering Group Department of Computer Science Hochschulstraße 10 Darmstadt64289 Germany

ISBN: (纸本)9781948087278

Automatic evaluation systems in the field of automatic summarization have been relying on the availability of gold standard summaries for over ten years. Gold standard summaries are expensive to obtain and often require the availability of domain experts to achieve high quality. In this paper, we propose an alternative evaluation approach based on pairwise preferences of sentences. In comparison to gold standard summaries, they are simpler and cheaper to obtain. In our experiments, we show that humans are able to provide useful feedback in the form of pairwise preferences. The new framework performs better than the three most popular versions of ROUGE with less expensive human input. We also show that our framework can reuse already available evaluation data and achieve even better results. © 2018 The Association for Computational Linguistics.

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Digital personal health libraries: A systematic literature review

arXiv

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arXiv 2020年

作者： Ding, Huitong Zhang, Chi An, Ning Zhang, Lingling Xie, Ning Alterovitz, Gil School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Department of Computer Science University of California Davis CA United States College of Nursing and Health Sciences University of Massachusetts Boston BostonMA United States Biomedical Cybernetics Laboratory Division of General Internal Medicine and Primary Care Department of Medicine Brigham and Women's Hospital BostonMA United States Department of Medicine Harvard Medical School BostonMA United States

Objective: This paper gives context on recent literature regarding the development of digital personal health libraries (PHL) and provides insights into the potential application of consumer health informatics in diverse clinical specialties. Materials and Methods: A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Here, 2,850 records were retrieved from PubMed and EMBASE in March 2020 using search terms: personal, health, and library. Information related to the health topic, target population, study purpose, library function, data source, data science method, evaluation measure, and status were extracted from each eligible study. In addition, knowledge discovery methods, including co-occurrence analysis and multiple correspondence analysis, were used to explore research trends of PHL. Results: After screening, this systematic review focused on a dozen articles related to PHL. These encompassed health topics such as infectious diseases, congestive heart failure, electronic prescribing. Data science methods included relational database, information retrieval technology, ontology construction technology. Evaluation measures were heterogeneous regarding PHL functions and settings. At the time of writing, only one of the PHLs described in these articles is available for the public while the others are either prototypes or in the pilot stage. Discussion: Although PHL researches have used different methods to address problems in diverse health domains, there is a lack of an effective PHL to meet the needs of older adults. Conclusion: The development of PHLs may create an unprecedented opportunity for promoting the health of older consumers by providing diverse health information. Copyright © 2020, The Authors. All rights reserved.

关键词： Population statistics

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Exploring Social Media Data for MOOC Recommendation

Exploring Social Media Data for MOOC Recommendation

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International Conference on Systems of Collaboration (SysCo)

作者： Sara Assami Najima Daoudi Rachida Ajhoun Department of Computer Science Smart Systems Laboratory Mohammed Vth University Rabat Morocco Department of Data and Knowledge Engineering Information Sciences School (ESI) Rabat Morocco

ISBN: (数字)9781728142166

ISBN: (纸本)9781728142173

MOOCs (Massive Open Online Courses) are a key tool for open education stakeholders and high-level universities to face the problem of limited resources and make education accessible worldwide. Our study relies on the hypothesis of the influence of learning adaptation on the learners' lack of motivation in MOOCs. In this sense, a recommendation approach could stimulate the learner's interest in specific MOOCs. In previous research, we have identified the recommendation criteria to recommend MOOCs adapted to the learner's needs and motivations. Then, we modeled the ontology of the learning actors' profiles for the matching of the characteristics of learners with adapted MOOCs. Afterward, we designed the functional architecture and use cases of a semantic recommendation system to stimulate learners' interest in MOOCs. However, the recommendation process is still hindered by the learner profile on these platforms that doesn't represent the learner's interests and motivations. Yet, social media profiles extraction can be an external data source for learners' profile enrichment. So, the purpose of this research is to use social media mining for the acquisition of personal and professional data about learners. Therefore, we apply the first phases of data mining on a social media dataset to explore the potentially meaningful data for MOOC recommendation.

关键词： Social networking (online) Data mining Educational courses Recommender systems computer aided instruction

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Tau influences the severity of delusions in older persons with early-stage cognitive decline

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Alzheimer's & Dementia 2023年第S2期19卷

作者： Marc A Khoury Mohamad-Ali Bahsoun Ayad Fahdel Shukrullah Shunbuli Saanika Venkatesh Abdollah Ghazvanchachi Samir Mitha Karissa Chan Luis R Fornazzari Nathan W Churchill Zahinoor Ismail David G. Munoz Tom A. Schweizer Alan R. Moody Corinne E. Fischer April Khademi St. Michaels Hospital Toronto ON Canada Keenan Research Centre for Biomedical Science the Li Ka Shing Knowledge Institute St. Michael’s Hospital Toronto ON Canada Toronto ON Canada Institute for Biomedical Engineering Science & Tech (iBEST) Toronto ON Canada Keenan research centre for biomedical research Li ka shing knowledge institute Toronto ON Canada Keenan Research Centre for Biomedical Research the Li Ka Shing Knowledge Institute St. Michael’s Hospital Toronto ON Canada Electrical Computer and Biomedical Engineering Department Ryerson University Toronto ON Canada Department of Medicine Neurology Division University of Toronto Toronto ON Canada Keenan Research Centre for Biomedical Science the Li Ka Shing Knowledge Institute St. Michael’s Hospital Toronto ON Canada Hotchkiss Brain Institute University of Calgary Calgary AB Canada Keenan Research Centre for Biomedical Research St. Michael’s Hospital Toronto ON Canada University of Toronto Toronto ON Canada Division of Pathology St. Michael’s Hospital Toronto ON Canada Division of Neurosurgery Faculty of Medicine University of Toronto Toronto ON Canada Division of Neurosurgery St. Michael’s Hospital Toronto ON Canada Institute of Medical Sciences University of Toronto Toronto ON Canada Institute of Biomaterials and Biomedical Engineering University of Toronto Toronto ON Canada St. Michael’s Hospital Toronto ON Canada Faculty of Medicine Department of Psychiatry University of Toronto Toronto ON Canada Ryerson University IAMLAB Department of Electrical Computer and Biomedical Engineering Toronto ON Canada

Background The relationship between delusional severity and structural brain changes in patients with cognitive decline is unclear. In prior work by our group, we conducted texture and volume analysis of Normal Appearing Brain Matter (NABM) using FLAIR MRI sequences in a cognitively impaired delusional cohort. Our results demonstrated that with more severe delusions, greater global brain disease burden and tissue degeneration in NABM is apparent. In this study we examined the relationship between our findings and neurodegenerative biomarkers, ApoE4 and plasma tau. Methods A total of 42 patients with Alzhiemer’s disease (AD) or Mild Cognitive Impairment (MCI) with delusions as identified by the Neuropsychiatric Inventory-Quick version (NPI-Q) were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). From fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) images, Brain extraction and intensity standardization alghorthims were applied to extract the normal-appearing brain matter (NABM). Three biomarkers were measured from the NABM region, which included volume and two texture features called integrity and damage that measure the structural integrity of tissue and tissue damage. Statistical analysis included a multivariable stepwise regression of each biomarker as a dependent variable across delusional severity groups 1 (lowest severity) to 3 (highest severity) and the inclusion of sex, age, education, ApoE4 and plasma tau as co-variates. Results Data were gathered on the 42 participants representing 164 MRI scans. Significant associations were found between baseline plasma tau, delusional severity group 3 for the NABM integrity and volume biomarkers (p<.05). Additionally, ApoE4 homozygotes were signficantly associated to NABM volume, integrity and damage, though no association was found with delusional severity. Conclusion These findings suggest that plasma tau may be linked to both delusional severity and structural alteratio

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Modeling method for temperature anomaly analysis 10th

Modeling method for temperature anomaly analysis

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10th International Joint Conference on Computational Intelligence, IJCCI 2018

作者： Kerdprasop, Kittisak Chuaybamroong, Paradee Kerdprasop, Nittaya Data and Knowledge Engineering Research Unit School of Computer Engineering Suranaree University of Technology Nakhon Ratchasima Thailand Department of Environmental Science Thammasat University Thailand

ISBN: (纸本)9789897583278

This study applies intelligent analytical methods to analyze temperature anomaly events during the past seven centuries of countries in the Southeast Asia including Thailand, Malaysia, Myanmar, and Cambodia. The temperature reconstruction during the years 1300 to 1999 were used as data source for anomaly analysis. In the analytical process, correlation analysis was applied to initially investigate the temperature variability concordance among the Southeast Asian countries. The results are that temperature variability patterns in Thailand, Myanmar, and Cambodia are moderately correlated to each other. On the contrary, the temperature variation patterns of Malaysia do not correlate to other countries in the same region. The further in-depth analysis focuses on the temperature anomaly of Thailand that shows high variability from the 14th to 16th centuries. Several machine learning algorithms had been applied to estimate the temperature anomaly of Thailand based on the anomaly events among the neighbors. The learned models reveal that Myanmar temperature anomaly most associate to the Thailand’s temperature variation. The performance of each model had been assessed and the results reveal that the chi-squared automatic interaction detection, or CHAID, is the best one with 0.624 correlation coefficient and relative error around 0.611. Copyright © 2018 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Machine learning

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Forest fire area estimation using support vector machine as an approximator 10th

Forest fire area estimation using support vector machine as ...

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10th International Joint Conference on Computational Intelligence, IJCCI 2018

作者： Kerdprasop, Nittaya Poomka, Pumrapee Chuaybamroong, Paradee Kerdprasop, Kittisak Data and Knowledge Engineering Research Unit School of Computer Engineering Suranaree University of Technology Nakhon Ratchasima Thailand Department of Environmental Science Thammasat University Thailand

ISBN: (纸本)9789897583278

Forest fire is critical environmental issue that can cause severe damage. Fast detection and accurate estimation of forest fire burned area can help firefighters to effectively control damage. Thus, the purpose of this paper is to apply state of the art data modeling method to estimate the area of forest fire burning using support vector machine (SVM) algorithm as a tool for area approximation. The dataset is real forest fires data from the Montesinho natural park in the northeast region of Portugal. The original dataset comprises of 517 records with 13 attributes. We randomly sample the data 10 times to obtain 10 data-subsets for building estimation models using two kinds of SVM kernel: radial basis function and polynomial function. The obtained models are compared against other proposed techniques to assess performances based on the two measurement metrics: mean absolute error (MAE) and root mean square error (RMSE). The experimental results show that our SVM predictor using polynomial kernel function can precisely estimate forest fire damage area with the MAE and RMSE as low as 6.48 and 7.65, respectively. These errors are less than other techniques reported in the literature. Copyright © 2018 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Fires

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A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining

A Coarse-to-Fine Multi-stream Hybrid Deraining Network for S...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Yanyan Wei Zhao Zhang Haijun Zhang Richang Hong Meng Wang Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science Harbin Institute of Technology (Shenzhen) Xili University Town Shenzhen China

Single image deraining task is still a very challenging task due to its ill-posed nature in reality. Recently, researchers have tried to fix this issue by training the CNN-based end-to-end models, but they still cannot extract the negative rain streaks from rainy images precisely, which usually leads to an over de-rained or under de-rained result. To handle this issue, this paper proposes a new coarse-to-fine single image deraining framework termed Multi-stream Hybrid Deraining Network (shortly, MH-DerainNet). To obtain the negative rain streaks during training process more accurately, we present a new module named dual path residual dense block, i.e., Residual path and Dense path. The Residual path is used to reuse com-mon features from the previous layers while the Dense path can explore new features. In addition, to concatenate different scaled features, we also apply the idea of multi-stream with shortcuts between cascaded dual path residual dense block based streams. To obtain more distinct derained images, we combine the SSIM loss and perceptual loss to preserve the per-pixel similarity as well as preserving the global structures so that the deraining result is more accurate. Extensive experi-ments on both synthetic and real rainy images demonstrate that our MH-DerainNet can deliver significant improvements over several recent state-of-the-art methods.

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