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arXiv 2019年

作者： Kroner, Alexander Senden, Mario Driessens, Kurt Goebel, Rainer Department of Cognitive Neuroscience Faculty of Psychology and Neuroscience Maastricht University Maastricht Netherlands Maastricht Brain Imaging Centre Faculty of Psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Data Science and Knowledge Engineering Faculty of Science and Engineering Maastricht University Maastricht Netherlands Amsterdam Netherlands

Predicting salient regions in natural images requires the detection of objects that are present in a scene. To develop robust representations for this challenging task, high-level visual features at multiple spatial scales must be extracted and augmented with contextual information. However, existing models aimed at explaining human fixation maps do not incorporate such a mechanism explicitly. Here we propose an approach based on a convolutional neural network pre-trained on a large-scale image classification task. The architecture forms an encoder-decoder structure and includes a module with multiple convolutional layers at different dilation rates to capture multi-scale features in parallel. Moreover, we combine the resulting representations with global scene information for accurately predicting visual saliency. Our model achieves competitive and consistent results across multiple evaluation metrics on two public saliency benchmarks and we demonstrate the effectiveness of the suggested approach on five datasets and selected examples. Compared to state of the art approaches, the network is based on a lightweight image classification backbone and hence presents a suitable choice for applications with limited computational resources, such as (virtual) robotic systems, to estimate human fixations across complex natural scenes. Our TensorFlow implementation is openly available at https://***/alexanderkroner/saliency. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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A study of adaptive population sizing in a self-adaptive differential evolution

Results in Control and Optimization

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Results in Control and Optimization 2025年 20卷

作者： Haldi Budiman Shir Li Wang Theam Foo Ng Amr S. Ghoneim Haidi Ibrahim Bahbibi Rahmatullah Faculty of Computing and Meta-Technology Universiti Pendidikan Sultan Idris 35900 Tanjong Malim Perak Malaysia Fakultas Teknologi Informasi Universitas Islam Kalimantan Muhammad Arsyad AL-Banjar Jl. Adhiyaksa No. 2 Kayutangi Banjarmasin 70123 Kalimantan Selatan Indonesia Data Intelligence and Knowledge Management (DILIGENT) Universiti Pendidikan Sultan Idris 35900 Tanjong Malim Perak Malaysia Centre for Global Sustainability Studies Level 5 Hamzah Sendut Library Universiti Sains Malaysia 11800 Minden Penang Malaysia Faculty of Computing and Artificial Intelligence Helwan University 11795 Cairo Ain Helwan Egypt School of Electrical and Electronic Engineering Universiti Sains Malaysia 14300 Nibong Tebal Penang Malaysia

Differential Evolution (DE) is extensively applied due to its simplicity, robustness, and computational efficiency. However, the performance of DE is influenced by several factors, including the nature of the problem, the specific algorithm variant, and user-defined settings. Numerous studies have explored adaptive parameter settings to reduce the sensitivity of DE’s performance to user inputs, parameter choices, and problem characteristics. DE’s ability to find optimal solutions depends on offspring generation and population diversity. One of the ways to improve DE’s population diversity is by adjusting the population size, either by introducing new individuals or eliminating existing ones. This work investigates the adaptation of population sizing of a self-adaptive differential evolution algorithm called Self-Adaptive Ensemble-based DE with Enhanced Population Sizing (SAEDE-EP). The adaptation of population sizing in SAEDE-EP is influenced by two parameters: the threshold value for stagnation comparison of the best individual over generations and the population size’s growth rate. The effect of these two parameters on population sizing adaptation is evaluated using 26 benchmark single-objective unconstrained optimization functions consisting of unimodal, multimodal, hybrid, and composition functions. SAEDE-EP is compared against 18 state-of-the-art evolutionary algorithms on 10 functions from the 100-Digit Challenge on CEC 2019 single-objective real parameter optimization. Additionally, SAEDE-EP is tested on 57 problems from the CEC-2020 Competitions on Real-World Single Objective Constrained Optimization. Comparative analysis indicates that SAEDE-EP performs well in single-objective unconstrained optimization problems with various characteristics and solves 86% of the real-world single-objective constrained optimization, requiring less computational time and less exhaustive effort to set parameters.

关键词： Differential evolution Adaptive parameters Ensemble Population size

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A Comparative Study of Two Complex Ontologies in Air Traffic Management

A Comparative Study of Two Complex Ontologies in Air Traffic...

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Conference on Digital Avionics Systems (DASC)

作者： Eduard Gringinger Richard M. Keller Audun Vennesland Christoph G. Schuetz Bernd Neumayr Corporate Research Frequentis AG Vienna Austria Intelligent Systems Division NASA Ames Research Center Moffet Field CA USA Software Engineering Safety and Security SINTEF Trondheim Norway Institute of Business Informatics - Data & Knowledge Engineering Johannes Kepler University Linz Linz Austria

ISBN: (数字)9781728106496

ISBN: (纸本)9781728114972

Over the past 25 years, multiple different data models have been introduced to standardize information management and facilitate data exchange and integration in the aviation domain. As a next step in the evolution of aviation data management, ontologies capturing the semantics (concepts, properties, and relationships) have been produced based on those models. In this paper, we describe a study comparing two recently released and independently-developed complex ontologies focused on Air Traffic Management (ATM) - the NASA ATM Ontology and an ontology derived from the ATM Information Reference Model. We develop a methodology for manually comparing two ontologies and identifying what we describe as exact, light, and mismatches between concepts in the two ontologies. We also describe a classification scheme that characterizes mismatches in terms of the general reason for the mismatch. This approach can be applied to improve existing ATM ontologies and foster interoperability, which will benefit aviation stakeholders.

关键词： Information Exchange Models ATM Information Reference Model NASA ATM Ontology Ontology Matching Ontology Air traffic management Automatic Teller Machines classification scheme MISMATCH aviation data exchange data models Information management

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

SSRN

引用

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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Prozessbezogenes informationsmanagement in precision dairy farming

Prozessbezogenes informationsmanagement in precision dairy f...

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Die Jahrestagung 2019 der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft - 2019 Annual Conference of the German Association for Informatics in Agriculture, Forestry and the Food Sector

作者： Iwersen, Michael Lidauer, Laura Berger, Alexandra Auer, Wolfgang Tomic, Dana Kathrin Schrefl, Michael Efrosinin, Dmitry Sturm, Valentin Gusterer, Erika Drillich, Marc Wischenbart, Martin Vetmeduni Vienna Universitätsklinik für Wiederkäuer Abteilung Bestandsbetreuung Veterinärplatz 1 WienA-1210 Austria Smartbow GmbH Jutogasse 3 WeibernA-4675 Austria JKU Linz Institut für Wirtschaftsinformatik - Data Knowledge Engineering Altenbergerstraße 69 LinzA-4040 Austria JKU Linz Institut für Stochastik Altenbergerstraße 69 LinzA-4040 Austria Josephinum Research Rottenhauser Straße 1 Wieselburg3250 Austria

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Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

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The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

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Digital personal health libraries: A systematic literature review

arXiv

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arXiv 2020年

作者： Ding, Huitong Zhang, Chi An, Ning Zhang, Lingling Xie, Ning Alterovitz, Gil School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Department of Computer Science University of California Davis CA United States College of Nursing and Health Sciences University of Massachusetts Boston BostonMA United States Biomedical Cybernetics Laboratory Division of General Internal Medicine and Primary Care Department of Medicine Brigham and Women's Hospital BostonMA United States Department of Medicine Harvard Medical School BostonMA United States

Objective: This paper gives context on recent literature regarding the development of digital personal health libraries (PHL) and provides insights into the potential application of consumer health informatics in diverse clinical specialties. Materials and Methods: A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Here, 2,850 records were retrieved from PubMed and EMBASE in March 2020 using search terms: personal, health, and library. Information related to the health topic, target population, study purpose, library function, data source, data science method, evaluation measure, and status were extracted from each eligible study. In addition, knowledge discovery methods, including co-occurrence analysis and multiple correspondence analysis, were used to explore research trends of PHL. Results: After screening, this systematic review focused on a dozen articles related to PHL. These encompassed health topics such as infectious diseases, congestive heart failure, electronic prescribing. data science methods included relational database, information retrieval technology, ontology construction technology. Evaluation measures were heterogeneous regarding PHL functions and settings. At the time of writing, only one of the PHLs described in these articles is available for the public while the others are either prototypes or in the pilot stage. Discussion: Although PHL researches have used different methods to address problems in diverse health domains, there is a lack of an effective PHL to meet the needs of older adults. Conclusion: The development of PHLs may create an unprecedented opportunity for promoting the health of older consumers by providing diverse health information. Copyright © 2020, The Authors. All rights reserved.

关键词： Population statistics

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Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery

arXiv

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arXiv 2025年

作者： Zhang, Yanbo Khan, Sumeer A. Mahmud, Adnan Yang, Huck Lavin, Alexander Levin, Michael Frey, Jeremy Dunnmon, Jared Evans, James Bundy, Alan Dzeroski, Saso Tegner, Jesper Zenil, Hector Allen Discovery Center Tufts University MedfordMA United States Living Systems Lab KAUST Thuwal Saudi Arabia Intelligent Infrastructure Team Network Rail United Kingdom Department for AI in Society Science and Technology Zuse Institute Berlin Germany NVIDIA Research United States Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence Thuwal23952 Saudi Arabia Pasteur Labs BrooklynNY United States Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA United States Department of Chemistry University of Southampton University Road Hampshire Southampton United Kingdom Department of Biomedical Data Science Stanford University StanfordCA United States Knowledge Lab Department of Sociology University of Chicago IL United States Santa Fe Institute NM United States School of Informatics The University of Edinburgh United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia Thuwal23955-6900 Saudi Arabia Unit of Computational Medicine Department of Medicine Center for Molecular Medicine Karolinska Institutet Karolinska University Hospital L8:05 StockholmSE-171 76 Sweden Thuwal23955-6900 Saudi Arabia Science for Life Laboratory Tomtebodavagen 23A SolnaSE-17165 Sweden Algorithmic Dynamics Lab Research Departments of Biomedical Computing and Digital Twins School of Biomedical Engineering and Imaging Sciences King’s College London United Kingdom King’s Institute for Artificial Intelligence London United Kingdom The Alan Turing Institute London United Kingdom Oxford Immune Algorithmics Oxford University Innovation London Institute for Healthcare Engineering London United Kingdom Cancer Interest Group Francis Crick Institute London United Kingdom

With recent Nobel Prizes recognising AI contributions to science, Large Language Models (LLMs) are transforming scientific research by enhancing productivity and reshaping the scientific method. LLMs are now involved in experimental design, data analysis, and workflows, particularly in chemistry and biology. However, challenges such as hallucinations and reliability persist. In this contribution, we review how Large Language Models (LLMs) are redefining the scientific method and explore their potential applications across different stages of the scientific cycle, from hypothesis testing to discovery. We conclude that, for LLMs to serve as relevant and effective creative engines and productivity enhancers, their deep integration into all steps of the scientific process should be pursued in collaboration and alignment with human scientific goals, with clear evaluation metrics. The transition to AI-driven science raises ethical questions about creativity, oversight, and responsibility. With careful guidance, LLMs could evolve into creative engines, driving transformative breakthroughs across scientific disciplines responsibly and effectively. However, the scientific community must also decide how much it leaves to LLMs to drive science, even when associations with ‘reasoning’, mostly currently undeserved, are made in exchange for the potential to explore hypothesis and solution regions that might otherwise remain unexplored by human exploration alone. Copyright © 2025, The Authors. All rights reserved.

关键词： Plants (botany)

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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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Poisoning attack against estimating from pairwise comparisons

arXiv

引用

arXiv 2021年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Big Data Mining and Knowledge Management The School of Economics and Management University of Chinese Academy of Sciences Beijing100049 China

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious comparisons into the training data to fool the target ranking algorithm. Such a technique is called "poisoning attack" in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attack on the pairwise ranking algorithms, which can be generally formalized as the dynamic and static games between the ranker and the attacker, and can be modeled as certain kinds of integer programming problems mathematically. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computational tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees including the existence of Nash equilibrium and the generalization ability bounds. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results with toxic data can be decreased dramatically. © 2021, CC BY-NC-ND.

关键词： Toys

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