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arXiv 2019年

作者： Dürrschnabel, Dominik Hanika, Tom Stumme, Gerd Berlin School of Library and Information Science Humboldt University of Berlin Berlin Germany Knowledge and Data Engineering Group University of Kassel Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Kassel Germany

Order diagrams are an important tool to visualize the complex structure of ordered sets. Favorable drawings of order diagrams, i.e., easily readable for humans, are hard to come by, even for small ordered sets. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although these methods produce satisfying results for some ordered sets, they unfortunately perform poorly in general. In this work we present the novel algorithm DimDraw to draw order diagrams. This algorithm is based on a relation between the dimension of an ordered set and the bipartiteness of a corresponding *** Codes 68R10 06A07 Copyright © 2019, The Authors. All rights reserved.

关键词： Drawing (graphics)

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Target localization intervention and prognosis evaluation for an individual with mild cognitive impairment

Brain‐X

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Brain‐X 2023年第3期1卷

作者： Weiping Wang Haiyan Zhao Chang He Yuanbo Cui Zhen Wang Alexander Hramov Ping Luan Xiong Luo Jipeng Ouyang Kurths Jürgen School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan Guangdong China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Contribution: Writing - review & editing Department of Neurology Peking University Third Hospital Beijing China Contribution: Resources Zhen Wang The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi 710072 China. Contribution: Writing - original draft The 6th Research Institute of China Electronics Corporation Beijing China The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi China Chang He School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China. Contribution: Investigation Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Saratov State Medical University Saratov Russia Contribution: Data curation Guangdong Second Provincial General Hospital & Health Science Center Shenzhen Guangdong China Department of Neurology Shunde Hospital Southern Medical University Foshan Guangdong China Institute of Physics Humboldt-University Berlin Germany Potsdam Institute for Climate Impact Research Potsdam Germany

Currently, no specific treatments are available for Alzheimer's disease (AD). Mild cognitive impairment (MCI), the preclinical stage of AD, has a high possibility of reversing symptoms through neural regulation. A state dynamics model for single brain regions was developed to simulate blood oxygen level-dependent signals in a patient with early mild cognitive impairment. Subsequently, the analysis of functional connections was used to comprehensively consider multiple complex network centralities to locate the intervention targets, and a multiple brain region collaborative control scheme was designed. Finally, the reliability and effectiveness of the intervention were verified at the brain region and subnetwork levels. This technique provides a basis for future clinical diagnosis and treatment of AD and MCI.

关键词： adaptive pinning control Alzheimer's disease BOLD signals computational modeling Sub_network

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BanFEL: A Blockchain Based Smart Contract for Fair and Efficient Lottery Scheme

BanFEL: A Blockchain Based Smart Contract for Fair and Effic...

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IEEE Conference on Dependable and Secure Computing

作者： Jiasheng Li Zijian Zhang Meng Li Beijing Engineering Research Center of Massive Language Information Beijing Institute of Technology Beijing China School of Computer Science University of Auckland Aukland New Zealand Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Hefei University of Technology Anhui China

Lottery is a game with many people's dreams. But corruptions of lottery centers make the lottery unfair. To address this unfair issue, fair lottery schemes have been studied for several years. In these schemes, delay functions or aggregation protocols can be used to generate the winning numbers fairly. However, to the best of our knowledge, none of the existing schemes can achieve the winning number generated randomly, while the randomness can be verified fairly. In this article, we first propose a [B]lockchain based sm[a]rt co[n]tract for [F]air and [E]fficient [L]ottery (BanFEL) scheme. We further present a winning number random generation smart contract, by which players submit the purchased numbers and the non-tampering property is protected. Security analysis and experiments show that the fairness of BanFEL is protected better than the existing works. Meanwhile, the verification cost of this scheme are at least 0.03s lower than the traditional Grumbach's scheme between 1000 and 10000 players.

关键词： Blockchain Smart contracts Security Mathematical model Privacy Information processing

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A Configurable Translation-Based Cross-Lingual Ontology Mapping System to Adjust Mapping Outcome

SSRN

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SSRN 2018年

作者： Fu, Bo Brennan, Rob O’Sullivan, Declan Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland

Ontologies are widely considered as the building blocks of the semantic web, and with them, comes the data interoperability issue. As ontologies are not necessarily always labelled in the same natural language, one way to achieve semantic interoperability is by means of cross-lingual ontology mapping. Translation techniques are often used as an intermediate step to translate the conceptual labels within an ontology. This approach essentially removes the natural language barrier in the mapping environment and enables the application of monolingual ontology mapping tools. This paper shows that the key to this translation-based approach to cross-lingual ontology mapping lies with selecting appropriate ontology label translations in a given mapping context. Appropriateness of the translations in the context of cross-lingual ontology mapping differs from the ontology localisation point of view, as the former aims to generate correct mappings whereas the latter aims to adapt specifications of conceptualisations to target communities. This paper further demonstrates that the mapping outcome using the translation-based cross-lingual ontology mapping approach is conditioned on the translations selected for the intermediate label translation step. In particular, this paper presents the design, implementation and evaluation of a novel cross-lingual ontology mapping system: SOCOM++. SOCOM++ provides configurable properties that can be manipulated by a user in the process of selecting label translations in an effort to adjust the subsequent mapping outcome. It is shown through the evaluation that for the same pair of ontologies, the mappings between them can be adjusted by tuning the translations for the ontology labels. This finding is not yet shown in previous research. © 2018, The Authors. All rights reserved.

关键词： Mapping

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CEPV: A Tree Structure Information Extraction and Visualization Tool for Big knowledge Graph

CEPV: A Tree Structure Information Extraction and Visualizat...

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IEEE International Conference on Big knowledge (ICBK)

作者： Shaojing Sheng Peng Zhou Xindong Wu Key Laboratory of Knowledge Engineering with Big Data Heifei Unversity of Technology Ministry of Education Heifei China School of Computer Science and Information Engineering Heifei University of Technology Heifei China School of Computer Science and Technology Anhui University Heifei China Mininglamp Academy of Sciences Mininglamp Technology Beijing China Institute of Big Konowledge Science Heifei University of Technology Heifei China

A large amount of data with rich semantic and structural information has been accumulated in many real-world applications. In order to effectively describe the concepts and connections in these data sets, knowledge graph was proposed as a tool to handle it. The genealogy is a typical tree structure data and can be stored in the knowledge graph. However, due to the complexity and increasing volume of the data, how to efficiently extract and visualize the customized information from the big knowledge graph is hence a challenge and worthy of in-depth study. Motivated by this, we propose a novel user-specified information extraction and visualization tool, named CEPV (the Customized information Extracting, Processing and Visualization tool), for converting the big graph structure data into a specified tree structure display. The main steps of CEPV are as follows: firstly, according to the requirements of users, extracting the specified data from the massive, complex, heterogeneous data as fewer times as possible, which can reduce the frequency of database access and improve the overall efficiency of the algorithm. Secondly, the fault tolerance mechanism and attribute judgment rules are executed to ensure the correctness during the data processing. Finally, the processed data with a complex relationship is presented to the user in multiple visualization models. The high availability and effectiveness of our proposed tool is verified on a big knowledge graph dataset.

关键词： data visualization data mining Tools databases data processing Fault tolerance Fault tolerant systems

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Adaptive structure-constrained robust latent low-rank coding for image recovery

arXiv

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arXiv 2019年

作者： Zhang, Zhao Wang, Lei Li, Sheng Wang, Yang Zhang, Zheng Zha, Zhengjun Wang, Meng School of Computer Science and Technology Soochow University Suzhou215006 China 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 University of Georgia 549 Boyd GSRC AthensGA30602 Shenzhen China School of Information Science and Technology University of Science and Technology of China Hefei China

In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accurately, AS-LRC seamlessly integrates an adaptive weighting based block-diagonal structure-constrained low-rank representation and the group sparse salient feature extraction into a unified framework. Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part. To enforce the block-diagonal structures adaptive to different real datasets for the low-rank recovery, AS-LRC clearly computes an auto-weighting matrix based on the locality-adaptive features and multiplies by the low-rank coefficients for direct minimization at the same time. This encourages the codes to be block-diagonal and can avoid the tricky issue of choosing optimal neighborhood size or kernel width for the weight assignment, suffered in most local geometrical structures-preserving low-rank coding methods. In addition, our AS-LRC selects the L2,1-norm on the projection for extracting group sparse features rather than learning low-rank features by Nuclear-norm regularization, which can make learnt features robust to noise and outliers in samples, and can also make the feature coding process efficient. Extensive visualizations and numerical results demonstrate the effectiveness of our AS-LRC for image representation and recovery. Copyright © 2019, The Authors. All rights reserved.

关键词： Recovery

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Partitioning vectors into quadruples: Worst-case analysis of a matching-based algorithm 29

Partitioning vectors into quadruples: Worst-case analysis of...

引用

29th International Symposium on Algorithms and Computation, ISAAC 2018

作者： Ficker, Annette M.C. Erlebach, Thomas Mihalák, Matúš Spieksma, Frits C.R. Faculty of Economics and Business KU Leuven Leuven Belgium Department of Informatics University of Leicester Leicester United Kingdom Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9783959770941

Consider a problem where 4k given vectors need to be partitioned into k clusters of four vectors each. A cluster of four vectors is called a quad, and the cost of a quad is the sum of the component-wise maxima of the four vectors in the quad. The problem is to partition the given 4k vectors into k quads with minimum total cost. We analyze a straightforward matching-based algorithm and prove that this algorithm is a23 -approximation algorithm for this problem. We further analyze the performance of this algorithm on a hierarchy of special cases of the problem and prove that, in one particular case, the algorithm is a54 -approximation algorithm. Our analysis is tight in all cases except one. © Annette M. C. Ficker, Thomas Erlebach, Matúš Mihalák, and Frits C. R. Spieksma;licensed under Creative Commons License CC-BY

关键词： Approximation algorithms

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Few-shot deep adversarial learning for video-based person re-identification

arXiv

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arXiv 2019年

作者： Wu, Lin Wang, Yang Yin, Hongzhi Wang, Meng Shao, Ling Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei230000 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230000 China University of Queensland St Lucia4072 Australia School of Computer Science and Information Engineering Hefei University of Technology Hefei230000 China Abu Dhabi United Arab Emirates

Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages. Existing methods rely on supervision signals to optimise a projected space under which the distances between inter/intra-videos are maximised/minimised. However, this demands exhaustively labelling people across camera views, rendering them unable to be scaled in large networked cameras. Also, it is noticed that learning effective video representations with view invariance is not explicitly addressed for which features exhibit different distributions otherwise. Thus, matching videos for person re-ID demands flexible models to capture the dynamics in time-series observations and learn view-invariant representations with access to limited labeled training samples. In this paper, we propose a novel few-shot deep learning approach to video-based person re-ID, to learn comparable representations that are discriminative and view-invariant. The proposed method is developed on the variational recurrent neural networks (VRNNs) and trained adversarially to produce latent variables with temporal dependencies that are highly discriminative yet view-invariant in matching persons. Through extensive experiments conducted on three benchmark datasets, we empirically show the capability of our method in creating view-invariant temporal features and state-of-the-art performance achieved by our method. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Wall Building in the Game of StarCraft with Terrain Considerations

Wall Building in the Game of StarCraft with Terrain Consider...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Martin L. M. Rooijackers Mark H. M. Winands Games & AI Group Department of Data Science and Knowledge Engineering Maastricht University Maastricht The Netherlands

ISBN: (纸本)9781538643600

StarCraft is a Real-Time Strategy game, which has a large state-space, is played in real-time, and commonly features two opposing players, capable of acting simultaneously. One of the aspects of the game is building walls. In this paper, we present an algorithm that can be used for wall building for an agent playing the game of StarCraft: Brood War.

关键词： Inductors Games Tiles Real-time systems Estimation Programming

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Collective fast delivery by energy-efficient agents

arXiv

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arXiv 2018年

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

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 ωithat defines the rate of energy consumption while travelling a distance in the graph, and a velocity νiwith which it can move. We are interested in schedules (operating the k agents) that result in a small delivery time Τ (time when the item arrives at t), and small total energy consumption ϵ. Concretely, we ask for a schedule that: either (i) Minimizes Τ, (ii) Minimizes lexicographically (Τ, ϵ) (prioritizing fast delivery), or (iii) Minimizes ϵ · Τ + (1 - ϵ) · ϵ, for a given ϵ ∈ (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 3-approximation algorithm for (iii) using a single agent. Copyright © 2018, The Authors. All rights reserved.

关键词： Mobile agents

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