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12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual Conference of the European Association for computer Graphics, EG 2019

作者： Yuan, Juefei Abdul-Rashid, Hameed Li, Bo Lu, Yijuan Schreck, Tobias Bui, Ngoc-Minh Do, Trong-Le Nguyen, Khac-Tuan Nguyen, Thanh-An Nguyen, Vinh-Tiep Tran, Minh-Triet Wang, Tianyang School of Computing Sciences and Computer Engineering University of Southern Mississippi United States Department of Computer Science Texas State University United States Institute of Computer Graphics and Knowledge Visualization Graz University of Technology Austria Faculty of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Software Engineering Lab Vietnam National University Ho Chi Minh City Viet Nam University of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Department of Computer Science and Information Technology Austin Peay State University United States

ISBN: (纸本)9783038680772

Sketch-based 3D scene retrieval is to retrieve 3D scene models given a user's hand-drawn 2D scene sketch. It is a brand new but also very challenging research topic in the field of 3D object retrieval due to the semantic gap in their representations: 3D scene models or views differ from non-realistic 2D scene sketches. To boost this interesting research, we organized a 2D Scene Sketch-Based 3D Scene Retrieval track in SHREC'18, resulting a SceneSBR18 benchmark which contains 10 scene classes. In order to make it more comprehensive, we have extended the number of the scene categories from the initial 10 classes in the SceneSBR2018 benchmark to 30 classes, resulting in a new and more challenging benchmark SceneSBR2019 which has 750 2D scene sketches and 3,000 3D scene models. Therefore, the objective of this track is to further evaluate the performance and scalability of different 2D scene sketch-based 3D scene model retrieval algorithms using this extended and more comprehensive new benchmark. In this track, two groups from USA and Vietnam have successfully submitted 4 runs. Based on 7 commonly used retrieval metrics, we evaluate their retrieval performance. We have also conducted a comprehensive analysis and discussion of these methods and proposed several future research directions to deal with this challenging research topic. Deep learning techniques have been proved their great potentials again in dealing with this challenging retrieval task, in terms of both retrieval accuracy and scalability to a larger dataset. We hope this publicly available benchmark, together with its evaluation results and source code, will further enrich and promote 2D scene sketch-based 3D scene retrieval research area and its corresponding applications. © 2019 The Author(s)

关键词： Scalability

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Retrospective motion correction of MR images using prior-assisted deep learning

arXiv

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arXiv 2020年

作者： Chatterjee, Soumick Sciarra, Alessandro Dünnwald, Max Oeltze-Jafra, Steffen Nürnberger, Andreas Speck, Oliver Department of Biomedical Magnetic Resonance Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke Univeristy Magdeburg Germany MedDigit Department of Neurology Medical Faculty University Hopspital Department of Biomedical Magnetic Resonance Otto-von-Guericke Univeristy Magdeburg Germany MedDigit Department of Neurology Medical Faculty University Hopspital Faculty of Computer Science Otto-von-Guericke Univeristy Magdeburg Germany MedDigit Department of Neurology Medical Faculty University Hopspital German Centre for Neurodegenerative Diseases Center for Behavioral Brain Sciences Magdeburg Germany Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke Univeristy Center for Behavioral Brain Sciences Magdeburg Germany Department of Biomedical Magnetic Resonance Otto-von-Guericke Univeristy German Centre for Neurodegenerative Diseases Leibniz Institute for Neurobiology Center for Behavioral Brain Sciences Magdeburg Germany

In MRI, motion artefacts are among the most common types of artefacts. They can degrade images and render them unusable for accurate diagnosis. Traditional methods, such as prospective or retrospective motion correction, have been proposed to avoid or alleviate motion artefacts. Recently, several other methods based on deep learning approaches have been proposed to solve this problem. This work proposes to enhance the performance of existing deep learning models by the inclusion of additional information present as image priors. The proposed approach has shown promising results and will be further investigated for clinical validity. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Abnormal behaviour detection at traffic junctions using Lucas Kanade and Harris Corner detector 4

Abnormal behaviour detection at traffic junctions using Luca...

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4th IEEE International Conference on Recent Advances in Information Technology, RAIT 2018

作者： Jain, Charvi Gautam, Diwakar Department of Computer Science and Engineering Indian Institute of Information Technology UnaHimachal Pradesh India Department of Computer Science and Engineering Sharda University Knowledge Park III Greater NoidaUttar Pradesh India

ISBN: (纸本)9781538630396

Image processing has played a vital role in every aspect of human life. Video surveillance has reached a major out through by the application of advanced image processing and frame modeling techniques. Video surveillance is the most recent issue regarding community security and welfare. Detection of behavior of moving objects in video streams is a vital aspect. In this article, automatic real-time object's behavior detection is implemented using Lucas Kanade and Harris Corner based approach. The velocity of moving element is reported and their association with anomalous activity is also an inseparable part of this art. This work can be used to develop a surveillance system of static camera and robotic automation visual systems. Whenever a new object comes in the camera frame, the system uses the concepts of frame based processing using Lucas Kanade approach incorporated with Harris Corner Detector. In this work, an investigation is carried out to define the optimum value of velocity parameters for declaring a behavior as anomalous. Moreover, it is analyzed that proposed algorithm for video behaviour detection at traffic points perform well but its accuracy can be further enhanced using three dimensional imaging and mapping of moving elements in a hyperspace. © 2018 IEEE.

关键词： Object detection

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

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

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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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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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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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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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