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An empirical evaluation of two general game systems: Ludii and RBG

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arXiv 2019年

作者： Piette, Éric Stephenson, Matthew Soemers, Dennis J.N.J. Browne, Cameron Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

Although General Game Playing (GGP) systems can facilitate useful research in Artificial Intelligence (AI) for game-playing, they are often computationally inefficient and somewhat specialised to a specific class of games. However, since the start of this year, two General Game Systems have emerged that provide efficient alternatives to the academic state of the art - the Game Description Language (GDL). In order of publication, these are the Regular Boardgames language (RBG), and the Ludii system. This paper offers an experimental evaluation of Ludii. Here, we focus mainly on a comparison between the two new systems in terms of two key properties for any GGP system: simplicity/clarity (e.g. human-readability), and efficiency. Copyright © 2019, The Authors. All rights reserved.

关键词： knowledge representation

LoGANv2: Conditional Style-Based Logo Generation with Generative Adversarial Networks

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arXiv 2019年

作者： Oeldorf, Cedric Spanakis, Gerasimos Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

—Domains such as logo synthesis, in which the data has a high degree of multi-modality, still pose a challenge for generative adversarial networks (GANs). Recent research shows that progressive training (ProGAN) and mapping network extensions (StyleGAN) enable both increased training stability for higher dimensional problems and better feature separation within the embedded latent space. However, these architectures leave limited control over shaping the output of the network, which is an undesirable trait in the case of logo synthesis. This paper explores a conditional extension to the StyleGAN architecture with the aim of firstly, improving on the low resolution results of previous research and, secondly, increasing the controllability of the output through the use of synthetic class-conditions. Furthermore, methods of extracting such class conditions are explored with a focus on the human interpretability, where the challenge lies in the fact that, by nature, visual logo characteristics are hard to define. The introduced conditional style-based generator architecture is trained on the extracted class-conditions in two experiments and studied relative to the performance of an unconditional model. Results show that, whilst the unconditional model more closely matches the training distribution, high quality conditions enabled the embedding of finer details onto the latent space, leading to more diverse output. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

Learning policies from self-play with policy gradients and MCTS value estimates

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arXiv 2019年

作者： Soemers, Dennis J.N.J. Piette, Éric Stephenson, Matthew Browne, Cameron Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

In recent years, state-of-the-art game-playing agents often involve policies that are trained in self-playing processes where Monte Carlo tree search (MCTS) algorithms and trained policies iteratively improve each other. The strongest results have been obtained when policies are trained to mimic the search behaviour of MCTS by minimising a cross-entropy loss. Because MCTS, by design, includes an element of exploration, policies trained in this manner are also likely to exhibit a similar extent of exploration. In this paper, we are interested in learning policies for a project with future goals including the extraction of interpretable strategies, rather than state-of-the-art game-playing performance. For these goals, we argue that such an extent of exploration is undesirable, and we propose a novel objective function for training policies that are not exploratory. We derive a policy gradient expression for maximising this objective function, which can be estimated using MCTS value estimates, rather than MCTS visit counts. We empirically evaluate various properties of resulting policies, in a variety of board games. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

Efficient Contact Tracing for pandemics using blockchain

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Informatics in Medicine Unlocked 2021年 26卷

作者： Bari, Nida Qamar, Usman Khalid, Ayesha Knowledge & Data Science Research Centre Computer & Software Engineering Department College of Electrical and Mechanical Engineering National University of Sciences and Technology (NUST) Islamabad Pakistan The Centre for Secure Information Technologies (CSIT) ECIT Institute Queen's University of Belfast Belfast BT7 1NN United Kingdom

Blockchain technologies have been benefiting many industries by being decentralized, secure and confidential. They offer great potential in pandemic impacted scenarios as well. Contact tracing helps to mitigate the transmission of disease by alerting people who may have been exposed so they can act on time to protect themselves. Contact tracing systems face some challenges related to issues of medical privacy, data security and transparency. Multiple researches show concern that contact tracing discourages people to seek medication because of the fear of loss of data, subsequent stigma, discrimination, or abuse. In this paper, we discuss how contact tracing can be improved using blockchain technology and could be able to solve these issues. The aim of our proposed system would be to reduce the impact of pandemic, to implement this merger of the blockchains and the Contact tracing app and to ensure user privacy and avoid data misuse by incorporating a symmetric key cryptographic mechanism. To tackle the scalability related issues of Blockchain this framework uses IPFS, a distributed file storage system also known as Interplanetary File System. This blockchain based solution system will enhance contact tracing by making it more stable, secure, performant, highly useable and above all effective in the fight against any pandemic. © 2021 The Authors

关键词： And pandemic Blockchain Contact tracing COVID-19 Distributed system Interplanetary file system

A resource usage prediction-based energy-aware scheduling algorithm for instance-intensive cloud workflows 14th

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A resource usage prediction-based energy-aware scheduling al...

14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Wang, Zhibin Wen, Yiping Zhang, Yu Chen, Jinjun Cao, Buqing School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology Xiangtan China Swinburne Data Science Research Institute Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9783030129804

The applications of instance-intensive workflow are widely used in e-commerce, advanced manufacturing, etc. However, existing studies normally do not consider the problem of reducing energy consumption by utilizing the characters of instance-intensive workflow applications. This paper presents a resource usage Prediction-based Energy-Aware scheduling algorithm, named PEA. Technically, this method improves the energy efficiency of instance-intensive cloud workflow by predicting resources utilization and the strategies of batch processing and load balancing. The efficiency and effectiveness of the proposed algorithm are validated by extensive experiments. © 2019, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Scheduling algorithms

Trustworthy preference completion in social choice

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arXiv 2020年

作者： Li, Lei Xue, Minghe H., Chen X., Wu The School of Computer Science and Technology University of Science and Technology of China China Mininglamp Academy of Sciences Mininglamp Technologies Beijing China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China

As from time to time it is impractical to ask agents to provide linear orders over all alternatives, for these partial rankings it is necessary to conduct preference completion. Specifically, the personalized preference of each agent over all the alternatives can be estimated with partial rankings from neighboring agents over subsets of alternatives. However, since the agents’ rankings are nondeterministic, where they may provide rankings with noise, it is necessary and important to conduct the trustworthy preference completion. Hence, in this paper firstly, a trust-based anchor-kNN algorithm is proposed to find k-nearest trustworthy neighbors of the agent with trust-oriented Kendall-Tau distances, which will handle the cases when an agent exhibits irrational behaviors or provides only noisy rankings. Then, for alternative pairs, a bijection can be built from the ranking space to the preference space, and its certainty and conflict can be evaluated based on a well-built statistical measurement Probability-Certainty Density Function. Therefore, a certain common voting rule for the first k trustworthy neighboring agents based on certainty and conflict can be taken to conduct the trustworthy preference completion. The properties of the proposed certainty and conflict have been studied empirically, and the proposed approach has been experimentally validated compared to state-of-arts approaches with several data sets. © 2020, CC BY.

关键词： Nearest neighbor search

The minority matters: a diversity-promoting collaborative metric learning algorithm 22

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The minority matters: a diversity-promoting collaborative me...

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

关键词：

Variability of Electrocardiographic Imaging Within and Between Leadsets

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Variability of Electrocardiographic Imaging Within and Betwe...

Computers in Cardiology (CinC)

作者： Job Stoks Bianca D van Rees Sanne A Groeneveld Diantha JM Schipaanboord Lennart Blom Rutger J Hassink Matthijs JM Cluitmans Ralf Peeters Paul GA Volders BIOMED Hasselt University Hasselt Belgium Maastricht University Medical Centre Maastricht the Netherlands University Medical Centre Utrecht the Netherlands Data Science and Knowledge Engineering Maastricht University Maastricht the Netherlands

ISBN: (数字)9781728173825

ISBN: (纸本)9781728111056

The variability of the inverse solution provided by electrocardiographic imaging (ECGI) is largely unknown when comparing different leadsets or (similar) beats. In four patients, we compared activation times (ATs), recovery times (RTs), and correlation coefficients during QRS complex and STT segment between: 1) consecutive sinus beats within one leadset, and 2) multiple beats for two leadsets. Furthermore, reasons behind differences in RT were investigated. Zero-th order Tikhonov regularization was used to reconstruct ventricular epicardial potentials. A spatiotemporal estimation method was then used to determine the ATs and RTs from the reconstructed epicardial electrograms. Inter-leadset differences were generally low for ATs, but exceeded intra-leadset beat-to-beat variations. RTs, however, showed larger variation independent of leadset. Differences in RTs between beats or leadsets could partially be explained by low T-wave amplitudes and high levels of noise, which suggests that RT determination may require more advanced methods in these cases. These findings increase our understanding of the consequences of electrode placement for the inverse solution, as well as our understanding of the complexities of recovery time estimation in ECGI.

关键词： Electrodes Estimation Imaging Lead Spatiotemporal phenomena Noise measurement Image reconstruction

Liver Segmentation in Time-resolved C-arm CT Volumes Reconstructed from Dynamic Perfusion Scans using Time Separation Technique

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Liver Segmentation in Time-resolved C-arm CT Volumes Reconst...

International Image Processing, Applications and Systems Conference (IPAS)

作者： Soumick Chatterjee Hana Haseljić Robert Frysch Vojtěch Kulvait Vladimir Semshchikov Bennet Hensen Frank Wacker Inga Brüsch Thomas Werncke Oliver Speck Andreas Nürnberger Georg Rose Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Department of Biomedical Magnetic Resonance Faculty of Nature Science Otto von Guericke University Magdeburg Magdeburg Germany Faculty of Electrical Engineering and Information Technology Institute for Medical Engineering Otto von Guericke University Magdeburg Magdeburg Germany Faculty of Electrical Engineering and Information Technology Research Campus STIMULATE Otto von Guericke University Magdeburg Magdeburg Germany Institute of Materials Physics Helmholtz-Zentrum hereon Geesthacht Germany Institute of Diagnostic and Interventional Radiology Hannover Medical School Hannover Germany Research Campus STIMULATE Otto von Guericke University Magdeburg Magdeburg Germany Institute for Laboratory Animal Science Hannover Medical School Hannover Germany Department of Biomedical Magnetic Resonance Faculty of Nature Science Otto von Guericke University Magdeburg German Center for Neurodegenerative Disease Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany

ISBN: (纸本)9781665462204

Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours. The time separation technique (TST) has been successfully used for modelling C-arm cone-beam computed tomography (CBCT) perfusion data. The reconstruction can be accompanied by the segmentation of the liver - for better visualisation and for generating comprehensive perfusion maps. Recently introduced Turbolift learning has been seen to perform well while working with TST reconstructions, but has not been explored for the time-resolved volumes (TRV) estimated out of TST reconstructions. The segmentation of the TRVs can be useful for tracking the movement of the liver over time. This research explores this possibility by training the multi-scale attention UNet of Turbolift learning at its third stage on the TRVs and shows the robustness of Turbolift learning since it can even work efficiently with the TRVs, resulting in a Dice score of $0.864\pm 0.004$ .

关键词： Training Image segmentation Computed tomography Computational modeling Motion segmentation Liver Imaging