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检索条件"丛书名=Lecture notes in artificial intelligence,"

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Design and Application of Mapping Model for Font Recommendation System Based on Contents Emotion Analysis 19th

Design and Application of Mapping Model for Font Recommendat...

引用

19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Ji, Young Seo Lim, Soon Bum Sookmyung Womens Univ Seoul 04310 South Korea Res Inst IT Convergence Seoul 04310 South Korea

ISBN: (纸本)9789819947607;9789819947614

Fonts are an important tool that can compensate for the absence of nonverbal and paralinguistic means that are reflected in real-world situations. However, selecting an appropriate font is a process that heavily relies on aesthetic sense and experiential judgment, making it difficult for the general public who are not proficient in using fonts. Therefore, in this study, we intend to implement a service that automatically recommends fonts that match the message when content such as facial expressions and sentences are entered. To this end, we designed an experiment to interpret the emotions associated with different fonts and a model to map the actual content and fonts. In the process of identifying the emotion of the font, We selected emotion keywords to verify the relevance of fonts and quantified their emotional impressions. Since the emotional criteria for content extracted using a deep learning emotional analysis model differed from those for fonts, we devised a new mapping method. We created a mapping model that calculates the correlation between each emotional criterion and determines similarity. We applied this model to confirm the relationship between the emotions of the content and the fonts and developed a system that recommends fonts.

关键词： Font Recommendation Emotion Analysis User Experiment

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Knowledge Distillation with Graph Neural Networks for Epileptic Seizure Detection 1

引用

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Zheng, Qinyue Venkitaraman, Arun Petravic, Simona Frossard, Pascal Ecole Polytech Fed Lausanne Signal Proc Lab LTS4 Lausanne Switzerland Embark Studios Stockholm Sweden

ISBN: (数字)9783031434273

ISBN: (纸本)9783031434266;9783031434273

Wearable devices for seizure monitoring detection could significantly improve the quality of life of epileptic patients. However, existing solutions that mostly rely on full electrode set of electroencephalogram (EEG) measurements could be inconvenient for every day use. In this paper, we propose a novel knowledge distillation approach to transfer the knowledge from a sophisticated seizure detector (called the teacher) trained on data from the full set of electrodes to learn new detectors (called the student). They are both providing lightweight implementations and significantly reducing the number of electrodes needed for recording the EEG. We consider the case where the teacher and the student seizure detectors are graph neural networks (GNN), since these architectures actively use the connectivity information. We consider two cases (a) when a single student is learnt for all the patients using pre-selected channels;and (b) when personalized students are learnt for every individual patient, with personalized channel selection using a Gumbel-softmax approach. Our experiments on the publicly available Temple University Hospital EEG Seizure Data Corpus (TUSZ) show that both knowledge-distillation and personalization play significant roles in improving performance of seizure detection, particularly for patients with scarce EEG data. We observe that using as few as two channels, we are able to obtain competitive seizure detection performance. This, in turn, shows the potential of our approach in more realistic scenario of wearable devices for personalized monitoring of seizures, even with few recordings.

关键词： Personalized seizure detection Graph neural networks Knowledge distillation

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Few-Shot Human Motion Prediction for Heterogeneous Sensors 27th

Few-Shot Human Motion Prediction for Heterogeneous Sensors

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Drumond, Rafael Rego Brinkmeyer, Lukas Schmidt-Thieme, Lars Univ Hildesheim Hildesheim Germany

ISBN: (纸本)9783031333767;9783031333774

Human motion prediction is a complex task as it involves forecasting variables over time on a graph of connected sensors. This is especially true in the case of few-shot learning, where we strive to forecast motion sequences for previously unseen actions based on only a few examples. Despite this, almost all related approaches for few-shot motion prediction do not incorporate the underlying graph, while it is a common component in classical motion prediction. Furthermore, state-of-the-art methods for few-shot motion prediction are restricted to motion tasks with a fixed output space meaning these tasks are all limited to the same sensor graph. In this work, we propose to extend recent works on few-shot time-series forecasting with heterogeneous attributes with graph neural networks to introduce the first few-shot motion approach that explicitly incorporates the spatial graph while also generalizing across motion tasks with heterogeneous sensors. In our experiments on motion tasks with heterogeneous sensors, we demonstrate significant performance improvements with lifts from 10.4% up to 39.3% compared to best state-of-the-art models. Moreover, we show that our model can perform on par with the best approach so far when evaluating on tasks with a fixed output space while maintaining two magnitudes fewer parameters.

关键词： Time-series forecasting Human motion prediction Few-shot learning

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Convergence of RBF Networks Regression Function Estimates and Classifiers 21st

Convergence of RBF Networks Regression Function Estimates an...

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21st International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Krzyzak, Adam Galkowski, Tomasz Partyka, Marian Concordia Univ Dept Comp Sci & Software Engn 1455 Maisonneuve Blvd West Montreal PQ H3G 1M8 Canada Westpomeranian Univ Technol Dept Elect Engn Ul Sikorskiego 37 PL-70313 Szczecin Poland Czestochowa Tech Univ Inst Computat Intelligence Al Armii Krajowej 36 PL-42200 Czestochowa Poland Opole Univ Technol Fac Prod Engn & Logist Dept Knowledge Engn Ul Ozimska 75 PL-45370 Opole Poland

ISBN: (纸本)9783031234910;9783031234927

In the paper convergence of the RBF network regression estimates and classifiers with so-called regular radial kernels is investigated. The parameters of the network are trained by minimizing the empirical risk on the training data. We analyze MISE convergence by utilizing the machine learning theory techniques such as VC dimension and covering numbers and the error bounds involving them. The performance of the normalized RBF network regression estimates is also tested in simulations.

关键词： Nonlinear regression Classification RBF networks Convergence VC dimension Covering numbers Simulations

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Do Minimal Complexity Least Squares Support Vector Machines Work? 10th

Do Minimal Complexity Least Squares Support Vector Machines ...

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10th IAPR TC3 workshop on artificial Neural Networks for Pattern Recognition (ANNPR)

作者： Abe, Shigeo Kobe Univ Nada Kobe Hyogo Japan

ISBN: (纸本)9783031206498;9783031206504

The minimal complexity support vector machine is a fusion of the support vector machine (SVM) and the minimal complexity machine (MCM), and results in maximizing the minimum margin and minimizing the maximum margin. It works to improve the generalization ability of the Ll SVM (standard SVM) and LP (Linear Programming) SVM. In this paper, we discuss whether it also works for the LS (Least Squares) SVM. The minimal complexity LS SVM (MLS SVM) is trained by minimizing the sum of squared margin errors and minimizing the maximum margin. This results in solving a set of linear equations and a quadratic program, alternatingly. According to the computer experiments for two-class and multiclass problems, the MLS SVM does not outperform the LS SVM for the test data although it does for the cross-validation data.

关键词： Support vector machines

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Improving Problem Decomposition and Regulation in Distributed Multi-Agent Path Finder (DMAPF) 24th

Improving Problem Decomposition and Regulation in Distribute...

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24th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA)

作者： Pianpak, Poom Tran Cao Son New Mexico State Univ Las Cruces NM 88003 USA

ISBN: (纸本)9783031212024;9783031212031

Distributed Multi-Agent Path Finder (DMAPF) is a novel distributed algorithm to solve the Multi-Agent Path Finding (MAPF) problem, where the objective is to find a sequence of movements for agents to reach their assigned locations without colliding with obstacles, which include other agents. The idea of DMAPF is to decompose a given MAPF problem into smaller sub-problems, then solve them in parallel. It has been shown that DMAPF can achieve higher scalability compared to centralized methods. This paper addresses two problems in the previous works. First, the previous works only divide problem maps in a simple, rectangular manner. This can create sub-problems with unbalanced numbers of locations in their maps when the shape of the original map is not rectangular or when the obstacles are not uniformly distributed. Having sub-problems that vary in sizes diminishes the effectiveness of parallelism. Second, the idea of DMAPF is to have agents move across sub-problems until they reach the sub-problems that contain their goals, but the previous works do not have a mechanism to regulate the number of agents residing in the sub-problems, thus it may fail to find the solution when a sub-problem is overcrowded. To mitigate the problems, we introduce (i) a method to decompose MAPF problems with balanced numbers of vertices;and (ii) a mechanism to regulate the number of agents in sub-problems. We also improve the performance of the Answer Set Programming (ASP) encoding, that was used in previous DMAPF implementations to solve MAPF sub-problem instances, by eliminating unnecessary parameters. The results show that the new solver scales better and is more efficient than the previous versions.

关键词： Multi agent systems

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Augmented Reality to Reduce Cognitive Load in Operational Decision-Making 17th

Augmented Reality to Reduce Cognitive Load in Operational De...

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17th International Conference on Augmented Cognition (AC)

作者： Moncur, Bethan Trigo, Maria J. Galvez Mortara, Letizia Univ Cambridge Inst Mfg Dept Engn Cambridge England Univ Lincoln Sch Comp Sci Lincoln England

ISBN: (纸本)9783031350160;9783031350177

Augmented reality (AR) technologies can overlay digital information onto the real world. This makes them well suited for decision support by providing contextually-relevant information to decision-makers. However, processing large amounts of information simultaneously, particularly in time-pressured conditions, can result in poor decision-making due to excess cognitive load. This paper presents the results of an exploratory study investigating the effects of AR on cognitive load. A within-subjects experiment was conducted where participants were asked to complete a variable-sized bin packing task with and without the assistance of an augmented reality decision support system (AR DSS). Semi-structured interviews were conducted to elicit perceptions about the ease of the task with and without the AR DSS. This was supplemented by collecting quantitative data to investigate if any changes in perceived ease of the task translated into changes in task performance. The qualitative data suggests that the presence of the AR DSS made the task feel easier to participants;however, there was only a statistically insignificant increase in mean task performance. Analysing the data at the individual level does not provide evidence of a translation of increased perceived ease to increased task performance.

关键词： Augmented Reality Decision-Making Decision Support System Cognitive Load Ease Performance

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A Topological Data Analysis Framework for Computational Phenotyping 21st

A Topological Data Analysis Framework for Computational Phen...

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21st International Conference on artificial intelligence in Medicine (AIME)

作者： Albi, Giuseppe Gerbasi, Alessia Chiesa, Mattia Colombo, Gualtiero, I Bellazzi, Riccardo Dagliati, Arianna Univ Pavia Dept Elect Comp & Biomed Engn Via Ferrata 5 I-27100 Pavia Italy Ctr Cardiol Monzino IRCCS Milan Italy Politecn Milan Dept Elect Informat & Biomed Engn Milan Italy

ISBN: (纸本)9783031343438;9783031343445

Topological Data Analysis (TDA) aims to extract relevant information from the underlying topology of data projections. In the healthcare domain, TDA has been successfully used to infer structural phenotypes from complex data by linking patients who display demographic, clinical, and biomarker similarities. In this paper we propose pheTDA, a TDA-based framework to assist the computational definition of novel phenotypes. More in details, the pheTDA (i) guides the application of the Topological Mapper algorithm to derive a robust data representation as a topological graph;(ii) identifies relevant subgroups of patients from the topology;(iii) assess discriminative features for each subgroup of patients via predictive models. We applied the proposed tool on a population of 725 patients with suspected coronary artery disease (CAD). pheTDA identified five novel subgroups, one of which is characterized by the presence of diabetic patients showing high cardiovascular risk score. In addition, we compare the results obtained with existing clustering algorithms, showing that pheTDA obtains better performance when compared to spectral decomposition followed by k-means.

关键词： Topological Data Analysis Computational phenotyping Coronary artery disease TDA Mapper

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Nominal AC-Matching 16th

Nominal AC-Matching

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16th International Conference on Intelligent Computer Mathematics (CICM)

作者： Ayala-Rincon, Mauricio Fernandez, Maribel Silva, Gabriel Ferreira Kutsia, Temur Nantes-Sobrinho, Daniele Univ Brasilia Brasilia Brazil Kings Coll London London England Johannes Kepler Univ Linz Linz Austria Imperial Coll London London England

ISBN: (纸本)9783031427527;9783031427534

The nominal syntax is an extension of the first-order syntax that smoothly represents languages with variable bindings. Nominal matching is first-order matching modulo alpha-equivalence. This work extends a certified first-order AC-unification algorithm to solve nominal AC-matching problems. To our knowledge, this is the first mechanically-verified nominal AC-matching algorithm. Its soundness and completeness were verified using the proof assistant PVS. The formalisation enriches the first-order AC-unification algorithm providing structures and mechanisms to deal with the combinatorial aspects of nominal atoms, permutations and abstractions. Furthermore, by adding a parameter for "protected variables" that cannot be instantiated during the execution, it enables nominal matching. Such a general treatment of protected variables also gives rise to a verified nominal AC-equality checker as a byproduct.

关键词： Nominal Matching Nominal AC-Matching Formal Methods PVS

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Erato: Automatizing Poetry Evaluation 22nd

Erato: Automatizing Poetry Evaluation

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22nd EPIA Conference on artificial intelligence (EPIA)

作者： Agirrezabal, Manex Oliveira, Hugo Goncalo Ormazabal, Aitor Univ Copenhagen CST Copenhagen Denmark Univ Coimbra CISUC DEI Coimbra Portugal Univ Basque Country UPV EHU Donostia San Sebastian Spain

ISBN: (纸本)9783031490101;9783031490118

We present Erato, a framework designed to facilitate the automated evaluation of poetry, including that generated by poetry generation systems. Our framework employs a diverse set of features, and we offer a brief overview of Erato's capabilities and its potential for expansion. Using Erato, we compare and contrast human-authored poetry with automatically-generated poetry, demonstrating its effectiveness in identifying key differences. Our implementation code and software are freely available under the GNU GPLv3 license.

关键词： Evaluation Poetry Automatic poetry evaluation

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