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30th Benelux Conference on Artificial Intelligence, BNAIC 2018

作者： Sironi, Chiara F. Winands, Mark H.M. Games and AI Group Department of Data Science and Knowledge Engineering Maastricht University Netherlands

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XTQA: Span-Level Explanations for Textbook Question Answering

arXiv

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arXiv 2020年

作者： Ma, Jie Chai, Qi Liu, Jun Yin, Qingyu Wang, Pinghui Zheng, Qinghua Ministry of Education of Key Lab for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering The School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Amazon United States

Textbook Question Answering (TQA) is the task of correctly answering diagram or non-diagram questions given large multi-modal contexts consisting of abundant essays and diagrams. In real-world scenarios, an explainable TQA system plays a key role in deepening humans’ understanding of learned knowledge. However, there is no work to investigate how to provide explanations currently. To address this issue, we devise a novel architecture towards span-level eXplanations for TQA (XTQA). In this paper, spans are the combinations of sentences within a paragraph. The key idea is to consider the entire textual context of a lesson as candidate evidence, and then use our proposed coarse-to-fine grained Explanation Extracting (EE) algorithm to narrow down the evidence scope and extract the span-level explanations with varying lengths for answering different questions. The EE algorithm can also be integrated into other TQA methods to make them explainable and improve the TQA performance. Experimental results show that XTQA obtains the best overall explanation result (mIoU) of 52.38% on the first 300 questions of CK12-QA test splits, demonstrating the explainability of our method (non-diagram: 150 and diagram: 150). The results also show that XTQA achieves the best TQA performance of 36.46% and 36.95% on the aforementioned splits respectively. We have released our code in https://***/dr-majie/*** Codes 68T07 Copyright © 2020, The Authors. All rights reserved.

关键词： Textbooks

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A deep belief network-based method to identify proteomic risk markers for Alzheimer’s disease

arXiv

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arXiv 2020年

作者： An, Ning Jin, Liuqi Ding, Huitong Yang, Jiaoyun Yuan, Jing Key Laboratory of Knowledge Engineering Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Neurology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Beijing China

While a large body of research has formally identified apolipoprotein E (APOE) as a major genetic risk marker for Alzheimer’s disease, accumulating evidence supports the notion that other risk markers may exist. The traditional Alzheimer’s-specific signature analysis methods, however, have not been able to make full use of rich protein expression data, especially the interaction between attributes. This paper develops a novel feature selection method to identify pathogenic factors of Alzheimer’s disease using the proteomic and clinical data. This approach has taken the weights of network nodes as the importance order of signaling protein expression values. After generating and evaluating the candidate subset, the method helps to select an optimal subset of proteins that achieved an accuracy greater than 90%, which is superior to traditional machine learning methods for clinical Alzheimer’s disease diagnosis. Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer’s disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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Conformal Stacked Weather Forecasting 7

Conformal Stacked Weather Forecasting

引用

7th Workshop on Conformal and Probabilistic Prediction and Applications, COPA 2018

作者： Neeven, Jelmer Smirnov, Evgueni Department of Data Science and Knowledge Engineering Maastricht University Maastricht6200MD Netherlands

In this paper we propose to apply the stacking method to aggregating multi-output predictions from different weather-forecasting domains (websites). Depending on the aggregating procedure (non-conformal/conformal), the results can be bare multi-output predictions or multi-output prediction regions. The experiments show the applicability of the stacking method on real data related to eight weather-forecasting domains. © 2018 J. Neeven & E. Smirnov.

关键词： Weather forecasting

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ReconResNet: Regularised residual learning for MR image reconstruction of undersampled cartesian and radial data

arXiv

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arXiv 2021年

作者： Chatterjee, Soumick Breitkopf, Mario Sarasaen, Chompunuch Yassin, Hadya Rose, Georg Nürnberger, Andreas Speck, Oliver Faculty of Computer Science Otto von Guericke University Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Otto von Guericke University Magdeburg Biomedical Magnetic Resonance Germany Research Campus Stimulate Otto von Guericke University Magdeburg Germany Institute for Medical Engineering Otto von Guericke University Magdeburg Germany German Center for Neurodegenerative Disease Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany Leibniz Institute for Neurobiology Magdeburg Germany

MRI is an inherently slow process, which leads to long scan time for high-resolution imaging. The speed of acquisition can be increased by ignoring parts of the data (undersampling). Consequently, this leads to the degradation of image quality, such as loss of resolution or introduction of image artefacts. This work aims to reconstruct highly undersampled Cartesian or radial MR acquisitions, with better resolution and with less to no artefact compared to conventional techniques like compressed sensing. In recent times, deep learning has emerged as a very important area of research and has shown immense potential in solving inverse problems, e.g. MR image reconstruction. In this paper, a deep learning based MR image reconstruction framework is proposed, which includes a modified regularised version of ResNet as the network backbone to remove artefacts from the undersampled image, followed by data consistency steps that fusions the network output with the data already available from undersampled k-space in order to further improve reconstruction quality. The performance of this framework for various undersampling patterns has also been tested, and it has been observed that the framework is robust to deal with various sampling patterns, even when mixed together while training, and results in very high quality reconstruction, in terms of high SSIM (highest being 0.990±0.006 for acceleration factor of 3.5), while being compared with the fully sampled reconstruction. It has been shown that the proposed framework can successfully reconstruct even for an acceleration factor of 20 for Cartesian (0.968±0.005) and 17 for radially (0.962±0.012) sampled data. Furthermore, it has been shown that the framework preserves brain pathology during reconstruction while being trained on healthy subjects. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Identifying Political Influencers on YouTube during the 2016 Moroccan General Election

引用

Procedia Computer science 2020年 170卷 1102-1109页

作者： Zakia Acharoui Altaf Alaoui Badia Ettaki Jamal Zerouaoui Mohamed Dakkon Laboratory of Engineering Sciences and Modeling Faculty of Sciences- Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering School of Information Sciences Rabat Morocco Département de Statistique et Informatique de gestion F.S.E.J.S. Universite Abdelmalek Essaadi Tetouan Morocco

The use of social media as a tool for political and electoral communication is not fully explored by Moroccan researchers. This study aims to identify the influencers of the political conversation, on YouTube, during the period of the Moroccan election campaign of 2016 by using centrality measures, and discuss the nature of information shared by those influencers and their profile. The finding shows that digital media and vloggers tend to be central instead of politician elites and official media who were absent from the top influencer list.

关键词： Centrality measures Social Network Analysis Electoral campaign Gatekeepers Modularity

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Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge

arXiv

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arXiv 2021年

作者： Sarasaen, Chompunuch Chatterjee, Soumick Breitkopf, Mario Rose, Georg Nürnberger, Andreas Speck, Oliver Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany Institute for Medical Engineering Otto von Guericke University Magdeburg Germany Research Campus Stimulate Otto von Guericke University Magdeburg Germany Faculty of Computer Science Otto von Guericke University Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany German Center for Neurodegenerative Disease Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany Leibniz Institute for Neurobiology Magdeburg Germany

Dynamic imaging is a beneficial tool for interventions to assess physiological changes. Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial resolution is compromised. To overcome this spatio-temporal trade-off, this research presents a super-resolution (SR) MRI reconstruction with prior knowledge based fine-tuning to maximise spatial information while reducing the required scan-time for dynamic MRIs. A U-Net based network with perceptual loss is trained on a benchmark dataset and fine-tuned using one subject-specific static high resolution MRI as prior knowledge to obtain high resolution dynamic images during the inference stage. 3D dynamic data for three subjects were acquired with different parameters to test the generalisation capabilities of the network. The method was tested for different levels of in-plane undersampling for dynamic MRI. The reconstructed dynamic SR results after fine-tuning showed higher similarity with the high resolution ground-truth, while quantitatively achieving statistically significant improvement. The average SSIM of the lowest resolution experimented during this research (6.25% of the k-space) before and after fine-tuning were 0.939 ± 0.008 and 0.957 ± 0.006 respectively. This could theoretically result in an acceleration factor of 16, which can potentially be acquired in less than half a second. The proposed approach shows that the super-resolution MRI reconstruction with prior-information can alleviate the spatio-temporal trade-off in dynamic MRI, even for high acceleration factors. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Applications of Game Theory in Vehicular Networks: A Survey

arXiv

引用

arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

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Collective fast delivery by energy-efficient agents 43

Collective fast delivery by energy-efficient agents

引用

43rd International Symposium on Mathematical Foundations of Computer science, MFCS 2018

作者： Bärtschi, Andreas Graf, Daniel Mihalák, Matúš Department of Computer Science ETH Zürich Switzerland Department of Data Science and Knowledge Engineering Maastricht University Netherlands

ISBN: (纸本)9783959770866

We consider k mobile agents initially located at distinct nodes of an undirected graph (on n nodes, with edge lengths). The agents have to deliver a single item from a given source node s to a given target node t. The agents can move along the edges of the graph, starting at time 0, with respect to the following: Each agent i has a weight ωi that defines the rate of energy consumption while travelling a distance in the graph, and a velocity υi with which it can move. We are interested in schedules (operating the k agents) that result in a small delivery time T (time when the item arrives at t), and small total energy consumption E. Concretely, we ask for a schedule that: either (i) Minimizes T , (ii) Minimizes lexicographically (T, E) (prioritizing fast delivery), or (iii) Minimizes · T + (1 − ) · E, for a given ∈ (0, 1). We show that (i) is solvable in polynomial time, and show that (ii) is polynomial-time solvable for uniform velocities and solvable in time O(n + k log k) for arbitrary velocities on paths, but in general is NP-hard even on planar graphs. As a corollary of our hardness result, (iii) is NP-hard, too. We show that there is a 2-approximation algorithm for (iii) using a single agent. © Andreas Bärtschi, Daniel Graf, and Matúš Mihalák.

关键词： Energy utilization

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Using mapping languages for building legal knowledge graphs from XML files

Using mapping languages for building legal knowledge graphs ...

引用

2019 Blockchain Enabled Semantic Web Workshop and Contextualized knowledge Graphs Workshop, BlockSW-CKG 2019

作者： Junior, Ademar Crotti Orlandi, Fabrizio O'Sullivan, Declan Dirschl, Christian Reul, Quentin ADAPT Centre for Digital Content Platform Research Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland Wolters Kluwer Deutschland Munchen Germany Wolters Kluwer N.V. Chicago United States

This paper presents our experience on building RDF knowledge graphs for an industrial use case in the legal domain. The information contained in legal information systems are often accessed through simple keyword interfaces and presented as a simple list of hits. In order to improve search accuracy one may avail of knowledge graphs, where the semantics of the data can be made explicit. Significant research effort has been invested in the area of building knowledge graphs from semi-structured text documents, such as XML, with the prevailing approach being the use of mapping languages. In this paper, we present a semantic model for representing legal documents together with an industrial use case. We also present a set of use case requirements based on the proposed semantic model, which are used to compare and discuss the use of state-of-the-art mapping languages for building knowledge graphs for legal data. © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： XML

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