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Virtual Campus (JICV), X International Conference on

作者： Mohammed EL FOUKI Noura AKNIN K. ED EL. KADIRI Information Technology and Modeling Systems Research Unit Computer Science Operational Research and Applied Statistics Laboratory Abdelmalek Essaadi University Tetouan Morocco Computer Science Operational Research and Applied Statistics Laboratory Abdelmalek Essaadi University Tetouan Morocco

ISBN: (数字)9781665415194

ISBN: (纸本)9781665430203

In recent years, artificial intelligence has been involved in the field of education in order to discover new knowledge about students and to improve educational environments. Machine learning, and more specifically data mining, is one of the most successful areas of artificial intelligence for educational data mining. In this study, we propose an advanced parameter optimization strategy based on deep learning model to predict students' performances for an e-learning platform operated by Abdelmalk Essaadi University.

关键词： Deep learning Electronic learning Education Predictive models Data mining Optimization

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DUT: Learning Video Stabilization By Simply Watching Unstable Videos

arXiv

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arXiv 2020年

作者： Xu, Yufei Zhang, Jing Maybank, Stephen J. Tao, Dacheng The School of Computer Science The Faculty of Engineering The University of Sydney 6 Cleveland St DarlingtonNSW2008 Australia The Department of Computer Science and Information Systems Birkbeck College United Kingdom

Previous deep learning-based video stabilizers require a large scale of paired unstable and stable videos for training, which are difficult to collect. Traditional trajectory-based stabilizers, on the other hand, divide the task into several sub-tasks and tackle them subsequently, which are fragile in textureless and occluded regions regarding the usage of handcrafted features. In this paper, we attempt to tackle the video stabilization problem in a deep unsupervised learning manner, which borrows the divide-and-conquer idea from traditional stabilizers while leveraging the representation power of DNNs to handle the challenges in real-world scenarios. Technically, DUT is composed of a trajectory estimation stage and a trajectory smoothing stage. In the trajectory estimation stage, we first estimate the motion of keypoints, initialize and refine the motion of grids via a novel multi-homography estimation strategy and a motion refinement network, respectively, and get the grid-based trajectories via temporal association. In the trajectory smoothing stage, we devise a novel network to predict dynamic smoothing kernels for trajectory smoothing, which can well adapt to trajectories with different dynamic patterns. We exploit the spatial and temporal coherence of keypoints and grid vertices to formulate the training objectives, resulting in an unsupervised training scheme. Experiment results on public benchmarks show that DUT outperforms state-of-the-art methods both qualitatively and quantitatively. The source code is available at https://***/Annbless/DUTCode. Copyright © 2020, The Authors. All rights reserved.

关键词： Trajectories

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Effects of the selection of characteristic values on the accuracy of results in the COMET method

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Procedia computer science 2020年 176卷 3581-3590页

作者： Bartłomiej Kizielewicz Joanna Kołodziejczyk Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Z.ołnierska 49 71-210 Szczecin Poland

This paper describes preliminary research on the relationship between the selection of characteristic values in the COMET method and the accuracy of the final ranking. Simulation studies on three test functions are presented. These functions are references to the conducted simulations and have been taken from the literature. Thanks to their application, human error can be excluded and focus only on methodical error. The presented experiment compares two approaches in determining characteristic values. For each function, 10000 randomly selected sets are generated and based on which the similarity of the ranking calculated by the COMET method with a reference ranking is calculated. The results are interpreted by means of box diagrams. The research showed the high effectiveness of both approaches and was used to determine the next directions for future works.

关键词： Optimization decision-making multi-criteria decision analysis COMET numerical analysis fuzzy logic

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Segmentation of the Left Atrium from 3D Gadolinium-Enhanced MR Images with Convolutional Neural Networks 9th

Segmentation of the Left Atrium from 3D Gadolinium-Enhanced ...

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9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and computer-Assisted Intervention, MICCAI 2018

作者： Preetha, Chandrakanth Jayachandran Haridasan, Shyamalakshmi Abdi, Vahid Engelhardt, Sandy Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Faculty of Computer Science and Research Campus STIMULATE University of Magdeburg Magdeburg Germany Department of Computer Science Mannheim University of Applied Sciences Mannheim Germany

ISBN: (纸本)9783030120283

Gadolinium contrast agents are used in a third of all magnetic resonance scans to study the extent of fibrosis across the left atria in patients with atrial fibrillation. Direct segmentation of the atrial heart chambers from these 3D gadolinium-enhanced magnetic resonance images (GE-MRI) is very demanding due to the low contrast between the atrial tissue and background. Current automatic segmentation methods delineate the left atrium on auxiliary bright-blood MRI scans, which need to be registered to GE-MRI in an additional, potentially error-prone step. Yet, it could render extremely useful to obtain direct segmentation on GE-MRI to simultaneously estimate the left atrium anatomy and the extent of fibrosis. In this work, we present a deep learning approach which is able to segment the left atrium from 3D GE-MRI. 100 data sets provided by the MICCAI 2018 Atrial Segmentation Challenge have been used to train and test deep convolutional neural networks (CNN), which follow a 2D architecture with deep supervision (train-validation-test 70-5-25). After performing a four fold cross validation, the network achieved a mean dice of 0.8945. Within the scope of the test phase of the challenge, we trained the network on 100 data sets, predicted novel segmentations on the official test set and, according to the leaderboard, achieved a final score of 0.888. © 2019, Springer Nature Switzerland AG.

关键词： Magnetic resonance

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An integrated decision support framework for exploring the barriers and potential application scenarios in metaverse hospitality

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Journal of Industrial information Integration 2025年 45卷

作者： Wu, Qun Tan, Weiqi Zhou, Ligang Deveci, Muhammet Pamucar, Dragan Pedrycz, Witold School of Mathematical Sciences Anhui University Hefei230601 China Anhui University Center for Applied Mathematics Anhui University Hefei Anhui230601 China Department of Industrial Engineering Turkish Naval Academy National Defence University Istanbul34942 Tuzla Turkey Royal School of Mines Imperial College London LondonSW7 2AZ United Kingdom Department of Information Technologies Western Caspian University Baku1001 Azerbaijan Faculty of Organizational Sciences University of Belgrade Belgrade11000 Serbia Department of Electrical and Computer Engineering University of Alberta Edmonton ABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland

The global hospitality industry is undergoing a profound transformation, primarily driven by the internet era within the context of Industry 4.0 and the continuous evolution of the metaverse. The metaverse offers limitless possibilities for creating immersive and personalized accommodation experiences by closely integrating the virtual with the real world. It also provides a new perspective and mindset for guiding the hospitality industry to rethink and redesign service models and business processes. Although the metaverse application in the hospitality industry has already made significant progress, this process still faces numerous barriers, and the specific application scenarios of the metaverse hospitality industry (MHI) remain unclear. This study aims to develop an integrated decision support framework for exploring the barriers and potential application scenarios in metaverse hospitality. First, the interval type-2 fuzzy sets (IT2FSs) is introduced to quantify the complex and uncertain information in the decision-making procedure. Second, the IT2F-BWM with the IT2F weighted averaging (IT2FWA) operator is proposed to calculate the barriers’ weights considering the group opinions. Third, the IT2FWA operator and IT2F-ExpTODIM method are incorporated to prioritize the potential application scenarios in MHI. Finally, a case study on the prioritization of barriers and application scenarios in MHI is utilized to test the effectiveness and practicality of the presented framework. Results show that the technology barrier (0.5217) and the interaction technology (0.2483) have the highest levels among the main and sub-main barrier hierarchies, respectively. The findings of this article may enrich the theories of uncertain decision-making methods and provide instructive suggestions for the operation and management of metaverse hospitality practice. © 2025 Elsevier Inc.

关键词： IT2F-BWM IT2F-ExpTODIM Metaverse hospitality Multi-criteria decision-making

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An exponential stability test for a messenger rna–micro rna ode model

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UPB Scientific Bulletin, Series A: applied Mathematics and Physics 2020年第4期82卷 11-16页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

In this paper we obtain a numerically tractable test (sufficient condition) for the exponential stability of the unique positive equilibrium point of an ODE system. The result (Theorem 3.1) is based on Lyapunov theory and Linear Ma-trix Inequalities techniques. The ODE model is related to the messengerRNA-microRNA interaction. © 2020, Politechnica University of Bucharest. All rights reserved.

关键词： RNA

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MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing 24

MRP 2020: The Second Shared Task on Cross-Framework and Cros...

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Shared Task: Cross-Framework Meaning Representation Parsing at 24th Conference on Computational Natural Language Learning, CoNLL 2020

作者： Oepen, Stephan Abend, Omri Abzianidze, Lasha Bos, Johan Hajič, Jan Hershcovich, Daniel Li, Bin O’Gorman, Tim Xue, Nianwen Zeman, Daniel University of Oslo Department of Informatics Norway The Hebrew University of Jerusalem School of Computer Science and Engineering Israel Utrecht University UiL OTS Netherlands University of Groningen Center for Language and Cognition Netherlands Charles University Prague Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Czech Republic University of Copenhagen Department of Computer Science Denmark Nanjing Normal University School of Chinese Language and Literature China University of Massachusetts at Amherst College of Information and Computer Sciences United States Brandeis University Department of Computer Science United States

ISBN: (纸本)9781952148644

The 2020 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and languages. Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization;for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework. The task received submissions from eight teams, of which two do not participate in the official ranking because they arrived after the closing deadline or made use of additional training data. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://***. © 2020 Association for Computational Linguistics.

关键词： Directed graphs

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Data augmentation in microscopic images for material data mining

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npj Computational Materials 2020年第1期6卷 601-609页

作者： Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China School of Materials Science and Technology Liaoning Technical UniversityLiaoning 114051China The Institute of Statistical Mathematics Research Organization of Information and SystemsTachikawaTokyo 190-8562Japan National Intellectual Property Administration Beijing 100088China Faculty of Engineering Sciences University of BurundiBujumburaBurundi Faculty of Engineering and Technology Palestine Technical University–KadoorieTulkaremPalestine College of Information Science and Engineering China University of PetroleumBeijingChina Key Lab of Petroleum Data Mining China University of PetroleumBeijingChina

Recent progress in material data mining has been driven by high-capacity models trained on large ***,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise ***,we develop a novel transfer learning strategy to address problems of small or insufficient *** strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining *** a specific task of grain instance image segmentation,this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the“image style”information in real *** results show that the model trained with the acquired synthetic data and only 35%of the real data can already achieve competitive segmentation performance of a model trained on all of the real *** the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data,our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation.

关键词： mining instance competitive

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An Exploration of K-Mer Ranges for Megahit in the Context of Microbial Community-Wide Identification of Antimicrobial Resistance Genes and Biosynthetic Gene Clusters

An Exploration of K-Mer Ranges for Megahit in the Context of...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Alina Cărunta Alexandru Eugeniu Mizeranschi Cristina Cărunta Horia Leonard Banciu Oana Teodora Moldovan Daniela Zaharie Viorel Negru Faculty of Mathematics and Computer Science West University of Timişoara Timişoara Romania Institute for Advanced Environmental Research West University of Timişoara Timişoara Romania Research and Development Station for Bovine Arad Arad Romania Department of Computers and Information Technology Politehnica University of Timişoara Timişoara Romania Department of Molecular Biology and Biotechnology Babeş-Bolyai University Cluj-Napoca Romania Centre for Systems Biology Biodiversity and Bioresources Babeş-Bolyai University Cluj-Napoca Romania Cluj-Napoca Department Emil Racoviţă Institute of Speleology Cluj-Napoca Romania

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

With the advent of genomics and next generation sequencing, metagenomics has become a discipline in its own right, enabling for the first time the study of complex microbial ecosystems containing species that cannot be cultured in the lab. Sequencing and characterizing these microbial genomes enables the identification of novel antimicrobial resistance genes (ARGs) and biosynthetic gene clusters (BGCs), with potential applications in industry. De novo sequence assembly, as a crucial step in metagenomics, is currently being implemented with tools such as MEGAHIT, which employ a multiple $k$ -mer approach, where several intermediate assemblies are built from k-mers from a range of sizes and merged at the end. The objective of this study was to investigate the impact that this range of k-mer sizes could have on a range of quality metrics, including the number of identified ARGs and BGCs, using a series of assembly scenarios involving 7 samples collected from the Movile Cave in Romania We found that larger k-mer sizes and more fine-grained ranges of intermediate steps were associated with increased metrics, including larger numbers of identified BGCs. Future studies are required in order to better understand the role of the range of k-mer sizes in MEGAHIT in the context of microbial communitywide identification of ARGs and BGCs.

关键词： Measurement Industries Sequential analysis Metagenomics Ecosystems Object recognition Bioinformatics Assembly Next generation networking Immune system

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Correction: Mitigating the worst parent attack in RPL based internet of things

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Cluster Computing 2022年第5期25卷 3765-3765页

作者： Sahay, Rashmi Geethakumari, G. Mitra, Barsha Department of CSE IcfaiTech (Faculty of Science and Technology) IFHE Hyderabad India Department of Computer Science and Information Systems BITS Pilani Hyderabad Campus Hyderabad India

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