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Can we detect harmony in artistic compositions? A machine learning approach

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arXiv 2020年

作者： Vandor, Adam van Vollenhoven, Marie Weiss, Gerhard Spanakis, Gerasimos Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Infinity Games Maastricht Netherlands

Harmony in visual compositions is a concept that cannot be defined or easily expressed mathematically, even by humans. The goal of the research described in this paper was to find a numerical representation of artistic compositions with different levels of harmony. We ask humans to rate a collection of grayscale images based on the harmony they convey. To represent the images, a set of special features were designed and extracted. By doing so, it became possible to assign objective measures to subjectively judged compositions. Given the ratings and the extracted features, we utilized machine learning algorithms to evaluate the efficiency of such representations in a harmony classification problem. The best performing model (SVM) achieved 80% accuracy in distinguishing between harmonic and disharmonic images, which reinforces the assumption that concept of harmony can be expressed in a mathematical way that can be assessed by humans. Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

Split Moves for Monte-Carlo Tree Search

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arXiv 2021年

作者： Kowalski, Jakub Mika, Maksymilian Pawlik, Wojciech Sutowicz, Jakub Szykula, Marek Winands, Mark H.M. University of Wroclaw Faculty of Mathematics and Computer Science Poland Maastricht University Department of Data Science and Knowledge Engineering Netherlands

In many games, moves consist of several decisions made by the player. These decisions can be viewed as separate moves, which is already a common practice in multi-action games for efficiency reasons. Such division of a player move into a sequence of simpler / lower level moves is called splitting. So far, split moves have been applied only in forementioned straightforward cases, and furthermore, there was almost no study revealing its impact on agents’ playing strength. Taking the knowledge-free perspective, we aim to answer how to effectively use split moves within Monte-Carlo Tree Search (MCTS) and what is the practical impact of split design on agents’ strength. This paper proposes a generalization of MCTS that works with arbitrarily split moves. We design several variations of the algorithm and try to measure the impact of split moves separately on efficiency, quality of MCTS, simulations, and action-based heuristics. The tests are carried out on a set of board games and performed using the Regular Boardgames General Game Playing formalism, where split strategies of different granularity can be automatically derived based on an abstract description of the game. The results give an overview of the behavior of agents using split design in different ways. We conclude that split design can be greatly beneficial for single- as well as multi-action games. © 2021, CC BY.

关键词： Efficiency

Deep learning for general game playing with ludii and polygames

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arXiv 2021年

作者： Soemers, Dennis J.N.J. Mella, Vegard Browne, Cameron Teytaud, Olivier Department of Data Science and Knowledge Engineering Maastricht University Netherlands Facebook AI Research United States

Combinations of Monte-Carlo tree search and Deep Neural Networks, trained through self-play, have produced state-of-the-art results for automated game-playing in many board games. The training and search algorithms are not game-specific, but every individual game that these approaches are applied to still requires domain knowledge for the implementation of the game’s rules, and constructing the neural network’s architecture – in particular the shapes of its input and output tensors. Ludii is a general game system that already contains over 500 different games, which can rapidly grow thanks to its powerful and user-friendly game description language. Polygames is a framework with training and search algorithms, which has already produced superhuman players for several board games. This paper describes the implementation of a bridge between Ludii and Polygames, which enables Polygames to train and evaluate models for games that are implemented and run through Ludii. We do not require any game-specific domain knowledge anymore, and instead leverage our domain knowledge of the Ludii system and its abstract state and move representations to write functions that can automatically determine the appropriate shapes for input and output tensors for any game implemented in Ludii. We describe experimental results for short training runs in a wide variety of different board games, and discuss several open problems and avenues for future research. © 2021, CC BY.

关键词： Domain knowledge

Cyclic generators and an improved linear kernel for the rooted subtree prune and regraft distance

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arXiv 2022年

作者： Kelk, Steven Linz, Simone Meuwese, Ruben Department of Data Science and Knowledge Engineering Maastricht University Netherlands School of Computer Science University of Auckland New Zealand

The rooted subtree prune and regraft (rSPR) distance between two rooted binary phylogenetic trees is a well-studied measure of topological dissimilarity that is NP-hard to compute. Here we describe an improved linear kernel for the problem. In particular, we show that if the classical subtree and chain reduction rules are augmented with a modified type of chain reduction rule, the resulting trees have at most 9k−3 leaves, where k is the rSPR distance;and that this bound is tight. The previous best-known linear kernel had size O(28k). To achieve this improvement we introduce cyclic generators, which can be viewed as cyclic analogues of the generators used in the phylogenetic networks literature. As a corollary to our main result we also give an improved weighted linear kernel for the minimum hybridization problem on two rooted binary phylogenetic trees. Copyright © 2022, The Authors. All rights reserved.

关键词： Bioinformatics

Benchmarking knowledge-based Context Delivery Systems

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Benchmarking Knowledge-based Context Delivery Systems

International Conference on Autonomic and Autonomous Systems (ICAS)

作者： J. Keeney D. Lewis D. O'Sullivan Knowledge & Data Engineering Group and CTVR Department of Computer Science Trinity College Dublin Dublin Ireland

As context-aware systems become more widespread and mobile there is an increasing need for a common distributed event platform for gathering context information and delivering to context-aware applications. The likely heterogeneity across the body of context information can be addressed using runtime reasoning over ontology-based context models. However, existing knowledge-based reasoning is not typically optimised for real-time operation so its inclusion in any context delivery platform needs to be carefully evaluated from a performance perspective. In this paper we propose a benchmark for knowledge-based context delivery platforms and in particular examine suitable knowledge benchmarks for assessing the ability of platforms to deal with semantic interoperability

关键词： Context-aware services Ontologies Pervasive computing Context modeling Temperature sensors Acoustic sensors knowledge management Publish-subscribe data engineering Computer science

Incremental processing of noisy user utterances in the spoken language understanding task

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arXiv 2019年

作者： Constantin, Stefan Niehues, Jan Waibel, Alex Karlsruhe Institute of Technology Institute for Anthropomatics and Robotic Maastricht University Department of Data Science and Knowledge Engineering

The state-of-The-Art neural network architectures make it possible to create spoken language understanding systems with high quality and fast processing time. One major challenge for real-world applications is the high latency of these systems caused by triggered actions with high executions times. If an action can be separated into subactions, the reaction time of the systems can be improved through incremental processing of the user utterance and starting subactions while the utterance is still being uttered. In this work, we present a model-Agnostic method to achieve high quality in processing incrementally produced partial utterances. Based on clean and noisy versions of the ATIS dataset, we show how to create datasets with our method to create low-latency natural language understanding components. We get improvements of up to 47.91 absolute percentage points in the metric F1-score. Copyright © 2019, The Authors. All rights reserved.

关键词： Network architecture

Transformer Embeddings for the Implementation of a Semantic MOOC Recommender System 6

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Transformer Embeddings for the Implementation of a Semantic ...

6th International Conference on Intelligent Computing in data sciences, ICDS 2024

作者： Assami, Sara Daoudi, Najima Ajhoun, Rachida Ecole Nationale Supérieure d'Informatique et d'Analyse Des Systèmes of Mohammed v University Department of Computer Science Rabat Morocco Ecole des Sciences de l'Information Department of Data and Knowledge Engineering Rabat Morocco

ISBN: (纸本)9798350351200

Semantic web technologies aim to interconnect objects using descriptors that identify their characteristics and improve information retrieval. In Massive Open Online Courses (MOOCs), the learning contents semantic links and their pertinence for learner needs are essential to motivate learners and reduce dropout rates. This work aims to develop a semantic recommender system prototype that uses external data sources on learners to identify their knowledge, skills, and interests to recommend MOOCs. First, this paper traces the knowledge-based recommendation of MOOCs and its data representation in the literature. Afterward, it explores a dataset of a MOOC platform to extract MOOCs content-related data, and a dataset of a professional social media to extract knowledge-based characteristics of a learner from his/her social profile. For the implementation of the semantic recommender, three different techniques: TF-IDF, GloVe, and USE models are used to compute the semantic similarity of learner needs and MOOCs content. The semantic MOOC recommender system implementation showed the advantages of using different data sources and the increased performance of a MOOC recommender system based on transformer models like the USE model. © 2024 IEEE.

关键词： Contrastive Learning

On transitive uncertainty mappings

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2nd International Conference on Rough Sets and knowledge Technology, RSKT 2007

作者： Baoqing, Jiang Keyun, Qin Zheng, Pei Institute of Data and Knowledge Engineering Henan University Kaifeng Henan 475001 China Department of Mathematics Southwest Jiaotong University Chengdu Sichuan 610031 China School of Mathematics and Computer Science Xihua University Chengdu Sichuan 610039 China

ISBN: (纸本)9783540724575

This paper is devoted to the discussion of transitive uncertainty mapping in general approximation space. It is proved that the best low-approximation mapping exist if the uncertainty mapping is transitive. Furthermore, the best low-approximation mapping is defined and its properties are discussed. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Rough set theory

Hierarchical features-based targeted aspect extraction from online reviews

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Intelligent data Analysis 2021年第1期25卷 205-223页

作者： He, Jin Li, Lei Wang, Yan Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei Anhui China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Department of Computing Macquarie University Sydney Australia MiningLamp Academy of Sciences Mininglamp Techonologies Beijing China

With the prevalence of online review websites, large-scale data promote the necessity of focused analysis. This task aims to capture the information that is highly relevant to a specific aspect. However, the broad scope of the aspects of the various products makes this task overarching but challenging. A commonly used solution is to modify the topic models with additional information to capture the features for a specific aspect (referred to as a targeted aspect). However, the existing topic models, either perform the full analysis to capture features as many as possible or estimate the similarity to capture features as coherent as possible, overlook the fine-grained semantic relations between the features, resulting in the captured features coarse and confusing. In this paper, we propose a novel Hierarchical Features-based Topic Model (HFTM) to extract targeted aspects from online reviews, then to capture the aspect-specific features. Specifically, our model can not only capture the direct features posing target-to-feature semantics but also capture the latent features posing feature-to-feature semantics. The experiments conducted on real-world datasets demonstrate that HFTMl outperforms the state-of-the-art baselines in terms of both aspect extraction and document classification. © 2021 - IOS Press. All rights reserved.

关键词： Aspect extraction Focused analysis Online reviews Text mining Topic modeling