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FC Portugal: RoboCup 2022 3D Simulation League and Technical Challenge Champions 25th

FC Portugal: RoboCup 2022 3D Simulation League and Technical...

引用

RoboCup Symposium

作者： Abreu, Miguel Kasaei, Mohammadreza Reis, Luis Paulo Lau, Nuno Univ Porto Artificial Intelligence & Comp Sci Lab LIACC Fac EngnLASIFEUP Porto Portugal Univ Edinburgh Sch Informat Edinburgh Scotland Univ Aveiro IEETA LASI DETI P-3810193 Aveiro Portugal

ISBN: (纸本)9783031284687;9783031284694

FC Portugal, a team from the universities of Porto and Aveiro, won the main competition of the 2022 RoboCup 3D Simulation League, with 17 wins, 1 tie and no losses. During the course of the competition, the team scored 84 goals while conceding only 2. FC Portugal also won the 2022 RoboCup 3D Simulation League Technical Challenge, accumulating the maximum amount of points by ending first in its both events: the Free/Scientific Challenge, and the Fat Proxy Challenge. The team presented in this year's competition was rebuilt from the ground up since the last RoboCup. No previous code was used or adapted, with the exception of the 6D pose estimation algorithm, and the get-up behaviors, which were re-optimized. This paper describes the team's new architecture and development approach. Key strategy elements include team coordination, role management, formation, communication, skill management and path planning. New lower-level skills were based on a deterministic analytic model and a shallow neural network that learned residual dynamics through reinforcement learning. This process, together with an overlapped learning approach, improved seamless transitions, learning time, and the behavior in terms of efficiency and stability. In comparison with the previous team, the omnidirectional walk is more stable and went from 0.70m/s to 0.90 m/s, the long kick from 15m to 19m, and the new close-control dribble reaches up to 1.41 m/s.

关键词： Motion planning

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Explainable artificial intelligence for Cytological Image Analysis 21st

Explainable Artificial Intelligence for Cytological Image An...

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

作者： Roehrl, Stefan Maier, Hendrik Lengl, Manuel Klenk, Christian Heim, Dominik Knopp, Martin Schumann, Simon Hayden, Oliver Diepold, Klaus Tech Univ Munich Chair Data Proc Munich Germany Tech Univ Munich Heinz Nixdorf Chair Biomed Elect Munich Germany

ISBN: (纸本)9783031343438;9783031343445

Emerging new technologies are entering the medical market. Among them, the use of Machine Learning (ML) is becoming more common. This work explores the associated Explainable artificial intelligence (XAI) approaches, which should help to provide insight into the often opaque methods and thus gain trust of users and patients as well as facilitate interdisciplinary work. Using the differentiation of white blood cells with the aid of a high throughput quantitative phase microscope as an example, we developed a web-based XAI dashboard to assess the effect of different XAI methods on the perception and the judgment of our users. Therefore, we conducted a study with two user groups of data scientists and biomedical researchers and evaluated their interaction with our XAI modules, with respect to the aspects of behavioral understanding of the algorithm, its ability to detect biases and its trustworthiness. The results of the user tests show considerable improvement achieved through the XAI dashboard on the measured set of aspects. A deep dive analysis aggregated on the different user groups compares the five implemented modules. Furthermore, the results reveal that using a combination of modules achieves higher appreciation than the individual modules. Finally, one observes a user's tendency of overestimating the trustworthiness of the algorithm compared to their perceived abilities to understand the behavior of the algorithm and to detect biases.

关键词： XAI Quantitative Phase Imaging Blood Cell Analysis

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Electricity Load and Peak Forecasting: Feature Engineering, Probabilistic LightGBM and Temporal Hierarchies 8th

Electricity Load and Peak Forecasting: Feature Engineering, ...

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8th International Workshop on Advanced Analytics and Learning on Temporal Data (AALTD) at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Rubattu, Nicolo Maroni, Gabriele Corani, Giorgio Dalle Molle Inst Artificial Intelligence IDSIA USI SUPSI CH-6962 Lugano Switzerland

ISBN: (纸本)9783031498954;9783031498961

We describe our experience in developing a predictive model that placed a high position in the BigDEAL Challenge 2022, an energy competition of load and peak forecasting. We present a novel procedure for feature engineering and feature selection, based on cluster permutation of temperatures and calendar variables. We adopted gradient boosting of trees and we enhanced its capabilities with trend modeling and distributional forecasts. We also included an approach to forecasts combination known as temporal hierarchies, which further improves the accuracy.

关键词： Load Forecasting Feature engineering Gradient Boosting Hierarchical Forecasting Forecast Reconciliation

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Contrastive Learning with Cluster-Preserving Augmentation for Attributed Graph Clustering

Contrastive Learning with Cluster-Preserving Augmentation fo...

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

作者： Zheng, Yimei Jia, Caiyan Yu, Jian Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China

ISBN: (纸本)9783031434112;9783031434129

Graph contrastive learning has attracted considerable attention and made remarkable progress in node representation learning and clustering for attributed graphs. However, existing contrastive-based clustering methods separate the processes of node representation learning and graph clustering into two stages, making it difficult to ensure good clustering. Therefore, it remains a challenge to design an effective contrastive learning method that jointly optimizes node representations and graph clustering. Moreover, existing random augmentation strategies to generate contrastive views may destroy the original topological structures of clusters in graphs. So it is crucial to construct an augmented graph that preserves the cluster structure of a given graph while benefitting graph clustering. To address these problems, we propose a contrastive learning method with cluster-preserving augmentation for attributed graph clustering. Specifically, we construct a contrasting view based on the generated kNN graph and edge betweenness centrality to preserve the inherent cluster structure of a graph. Then, a multilevel contrastive mechanism is proposed to maximize the agreement between node representations in multiple latent spaces. Finally, the objective of node representation learning is jointly optimized with the self-supervised clustering objective to obtain cluster distributions and discriminative node representations simultaneously. Extensive experiments on seven widely used real-world graphs demonstrate that the proposed model consistently outperforms existing state-of-the-art methods on clustering tasks.

关键词： Augmentation Contrastive learning Attributed graph Clustering

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Fast Visual Imperfection Detection when Real Negative Examples are Unavailable 22nd

Fast Visual Imperfection Detection when Real Negative Exampl...

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22nd International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Najgebauer, Patryk Scherer, Rafal Grycuk, Rafal Walczak, Jakub Wojciechowski, Adam Lada-Tondyra, Ewa Czestochowa Tech Univ Dept Intelligent Comp Syst Armii Krajowej 36 PL-42200 Czestochowa Poland Lodz Univ Technol Inst Informat Technol Lodz Poland Czestochowa Tech Univ Fac Elect Engn Al Armii Krajowej 36 PL-42200 Czestochowa Poland

ISBN: (纸本)9783031425073;9783031425080

We present a method of detecting defects and impurities in materials on an example of knitted fabrics. We describe problems related to the real-time imaging detection system. Furthermore, we propose a new fast, small fully convolutional networks with a special configuration for taking into account even small impurities. We also present a method of pattern learning having only small datasets without many defects. That can be connected with difficulties in physically preparing material samples for a traditional, full-fledged training set. The experiments demonstrated high accuracy and real-time speed of the proposed method.

关键词： Fully convolutional networks Image recognition Knitted fabric Defects detection

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Neural Machine Translation with Diversity-Enabled Translation Memory 15th

Neural Machine Translation with Diversity-Enabled Translatio...

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15th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Quang Chieu Nguyen Xuan Dung Doan Van-Vinh Nguyen Khac-Hoai Nam Bui Viettel Grp Viettel Cyberspace Ctr Hanoi Vietnam Vietnam Natl Univ Hanoi Hanoi Vietnam

ISBN: (纸本)9789819958337;9789819958344

Neural machine translation (NMT) using translation memory (TM) has been introduced as an emergent technique for improving machine translation systems (MTS). In this study, we propose an end-to-end NMT model with TM by exploiting the diversity of the retrieval-augmented phase using maximal marginal relevance (MMR). In particular, the proposed model is designed with monolingual TM, which is able to support low-resource scenarios. Furthermore, the memory retriever and translation models are jointly trained to improve translation performance. For the experiment, we use IWSLT15 (En <-> Vi) as a benchmark dataset to evaluate the performance of the proposed method. Accordingly, the experiential results show the effectiveness of the proposed method compared with strong baselines in this research field.

关键词： Neural Machine Translation Translation Memory Maximal Marginal Relevance Low Resource Language

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Assessment of Pharmaceutical Patent Novelty with Siamese Neural Networks 10th

Assessment of Pharmaceutical Patent Novelty with Siamese Neu...

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

作者： El-Shimy, Heba Zantout, Hind Hassen, Hani Ragab Heriot Watt Univ Dubai U Arab Emirates

ISBN: (纸本)9783031206498;9783031206504

Patents in the pharmaceutical field fulfil an important role as they contain details of the final product that is the culmination of years of research and possibly millions of dollars of investment. It is crucial that both patent producers and consumers are able to assess the novelty of such patents and perform basic processing on them. In this work, we review approaches in the literature in patent analysis and novelty assessment that range from basic digitisation to deep learning-based approaches including natural language processing, image processing and chemical structure extraction. We propose a system that automates the process of patent novelty assessment using Siamese neural networks for similarity detection. Our system showed promising results and has a potential to improve upon the current patent analysis methods, specifically in the pharmaceutical field, by not just focusing on the task from a Natural Language Processing perspective, but also, adding image analysis and adaptations for chemical structure extraction.

关键词： Document analysis Siamese neural networks CNN LSTM Optical character recognition Pharmaceutical patents Chemical structure extraction

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Coherence of COVID-19 Mortality of Spain Versus Western European Countries 18th

Coherence of COVID-19 Mortality of Spain Versus Western Euro...

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18th International Conference on Hybrid artificial intelligence Systems

作者： Badiola, Goizalde Grana, Manuel Lopez-Guede, Jose Manuel Univ Basque Country Grp Inteligencia Computac Bilbao Spain

ISBN: (纸本)9783031407246;9783031407253

The mortality impact of COVID-19 across European countries is a complex phenomenon that appears to be influenced by political, social, economic, and environmental factors. The western European countries have implemented similar policies, with some specific minor divergences, like in the implementations of lockdowns and vaccination passes, so that similar results were to be expected. We use the wavelet transform coherence (WCT) in order to assess the correlation at diverse time scales among COVID-19 mortality time series. The same process was carried out over the time series of stringency index of implemented population control polices. Specifically, we consider the pairwise WCTs of the COVID mortality and the stringency time series of western European countries versus Spain. We discuss the findings on the WCT plots regarding the temporal inhomogeneity of the plots, and the lack of correspondence between WCT plot dissimilarity and geographical distance of countries relative to Spain. This discussion raises questions about the hypothesis of the transmission of a specific pathogen as the main mechanism underlying the mortality attributed to COVID-19. The synchronization of non-pharmaceutical interventions in the first epoch is highly correlated to the synchronization of the mortality waves in almost countries. As an indication of the impact of political measures on COVID-19 mortality, we compute the overall correlation between second order correlation between mortality time series and stringency index time series, finding a highly significant positive correlation (0.53, p << 0.05), raising the question if stronger political measures may have lead to increased COVID-19 mortality.

关键词： Wavelet coherence COVID-19 Propagation

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Coherent Upper Conditional Previsions Defined by Fractal Outer Measures to Represent the Unconscious Activity of Human Brain 20th

Coherent Upper Conditional Previsions Defined by Fractal Out...

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20th International Conference on Modeling Decisions for artificial intelligence (MDAI)

作者： Doria, Serena Selmi, Bilel Univ G DAnnunzio Dept Engn & Geol Chieti Pescara Italy Univ Monastir Fac Sci Monastir Dept Math Anal Probabil & Fractals Lab LR18ES17 Monastir 5000 Tunisia

ISBN: (纸本)9783031334979;9783031334986

A mathematical model of coherent upper conditional previsions defined by fractal outer measures is proposed to represent the unconscious activity of human brain in AI. By adopting this specific Bayesian approach to human behaviour and reasoning we aim to provide a mathematical representation of fundamental functions of the human brain - usually considered as biased and detrimental to an account of normative rationality - without incurring the usual inconsistencies. In particular, it is proven that the model of the bias of selective attention described in the so-called Invisible Gorilla experiment, is often taken as a typical example of the limitations of human perception.

关键词： Coherent upper conditional previsions Fractal measures Unexpected events Unconscious activity Selective attention

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ADM in the European Union: An Interoperable Solution 15th

ADM in the European Union: An Interoperable Solution

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15th International Conference on Flexible Query Answering Systems (FQAS)

作者： Tassinari, Francesca European Commiss Jurid Sci B-1049 Brussels Belgium

ISBN: (纸本)9783031429347;9783031429354

Regulations (EU) 817 and 818 of 2019 establish a framework for the interoperability of six large-scale IT systemswithin theArea of Freedom, Security, and Justice. These Regulations bring significant changes to these systems' infrastructure by introducing four main components. This paper focuses on the analyse of a specific one, namely the Multiple-Identity Detector (MID) and the procedure thereto. The MID compares individuals' biometric, identity, and travel document data stored in the shared Biometric Matching Service (sBMS), the Common Identity Repository (CIR), and the Schengen Information System (SIS) according to probabilistic matching. The comparison might result in the automated generation of white or yellow links: White links indicate that the data belong to the same person;yellow links call for the human verification of the competent authority to establish a white, green, or red link. To date, it is not clear if the MID triggers decisions based solely on automated processing and, if so, to what extent they impact individuals. By shedding light on its functioning, this paper analyses whether data subjects could claim the right not to be subject to such links.

关键词： Automated-Decision Making Multiple-Identity Detector Interoperability Area of Freedom Security and Justice

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