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arXiv 2020年

作者： van Glabbeek, R.J. Middelburg, C.A. Data61 CSIRO Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia Informatics Institute Faculty of Science University of Amsterdam Amsterdam Netherlands

In most presentations of ACP with guarded recursion, recursive specifications are finite or infinite sets of recursion equations of which the right-hand sides are guarded terms. The completeness with respect to bisimulation equivalence of the axioms of ACP with guarded recursion has only been proved for the special case where recursive specifications are finite sets of recursion equations of which the right-hand sides are guarded terms of a restricted form known as linear terms. In this note, we widen this completeness result to the general case. Copyright © 2020, The Authors. All rights reserved.

关键词： Specifications

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Design and Implementation of a Radar-Camera Fusion System for Human Detection and Its Distance Measurement

Design and Implementation of a Radar-Camera Fusion System fo...

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International Electronics Symposium (IES)

作者： Indra Putra Mahayuda Alfan Rizaldy Pratama Bima Sena Bayu Dewantara Dadet Pramadihanto Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Data Science Department Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jawa Timur Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

Human distance detection is essential for improving safety and efficiency in applications such as autonomous vehicles, surveillance, and human-robot interaction. Integrating radar and camera data through sensor fusion significantly improves detection accuracy and reliability while reducing the inherent limitations of each sensor type. This research presents the design of a radar-camera fusion system that reads and processes data from both sources. Instance segmentation is used to identify objects for camera data, while radar data is projected to pixel coordinates through coordinate transformation, allowing the two data sets to be fused into the same space. This fusion results in more complete object information. The developed system achieved 100% object detection accuracy in the experimental scenario and demonstrated 93.4% distance measurement accuracy, with average longitudinal and latitudinal errors of 37.1 cm and 17.5 cm, respectively.

关键词： Instance segmentation Accuracy Surveillance Spaceborne radar Radar detection Sensor fusion Cameras

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MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence

arXiv

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arXiv 2021年

作者： Giziński, Stanislaw Kuźba, Michal Pieliński, Bartosz Sienkiewicz, Julian Laniewski, Stanislaw Biecek, Przemyslaw MI2 Data Lab Faculty of Mathematics and Information Science Warsaw University of Technology Faculty of Mathematics Informatics and Mechanics University of Warsaw Faculty of Political Science and International Studies University of Warsaw Faculty of Physics Warsaw University of Technology Quantitative Psychology and Economics Faculty of Economics University of Warsaw

The growing number of AI applications, also for high-stake decisions, increases the interest in Explainable and Interpretable Machine Learning (XI-ML). This trend can be seen both in the increasing number of regulations and strategies for developing trustworthy AI and the growing number of scientific papers dedicated to this topic. To ensure the sustainable development of AI, it is essential to understand the dynamics of the impact of regulation on research papers as well as the impact of scientific discourse on AI-related policies. This paper introduces a novel framework for joint analysis of AI-related policy documents and eXplainable Artificial Intelligence (XAI) research papers. The collected documents are enriched with metadata and interconnections, using various NLP methods combined with a methodology inspired by Institutional Grammar. Based on the information extracted from collected documents, we showcase a series of analyses that help understand interactions, similarities, and differences between documents at different stages of institutionalization. To the best of our knowledge, this is the first work to use automatic language analysis tools to understand the dynamics between XI-ML methods and regulations. We believe that such a system contributes to better cooperation between XAI researchers and AI policymakers. Copyright © 2021, The Authors. All rights reserved.

关键词： Planning

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Developing a Visual Dashboard to Improve Research Activity Assessment

Developing a Visual Dashboard to Improve Research Activity A...

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International Conference on Computer, Control, informatics and Its Applications (IC3INA)

作者： Muhammad Zaki Nur Rahman Esa Prakasa Moch Arif Bijaksana Bachelor of Data Science Faculty of Informatics Telkom University Bandung Indonesia National Research and Innovation Agency (BRIN) Research Center for Data and Information Sciences Bandung Indonesia School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

In research institutions, the ability to monitor and analyze publication output is crucial for assessing productivity and achieving strategic goals. This paper presents a dashboard designed to visualize and analyze publication data from a research center of the Indonesian public institution. The dashboard addresses several key questions: (1) whether the current number of publications is on track to meet the institution’s targets, (2) the comparison between journal and proceeding publications, (3) individual researcher productivity, (4) research group productivity, and (5) the collaborative efforts among researchers and groups. By leveraging this tool, stakeholders can gain insights into publication trends and identify areas for improvement. The findings reveal significant trends in publication types and collaboration patterns, offering a comprehensive overview of the institution’s research output. This study contributes to the understanding of research productivity metrics and their implications for strategic planning in academic research.

关键词： Productivity Measurement Visualization Target tracking Collaboration data visualization Strategic planning Market research Stakeholders Monitoring

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Attitudes and Acceptance Towards Artificial Intelligence in Medical Care 32

Attitudes and Acceptance Towards Artificial Intelligence in ...

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32nd Medical informatics Europe Conference, MIE 2022

作者： Holzner, Dana Apfelbacher, Timo Rödle, Wolfgang Schüttler, Christina Prokosch, Hans-Ulrich Mikolajczyk, Rafael Negash, Sarah Kartschmit, Nadja Manuilova, Iryna Buch, Charlotte Gundlack, Jana Christoph, Jan Department of Medical Informatics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Institute for Medical Epidemiology Biometrics and Informatics Interdisciplinary Center for Health Sciences Faculty of Medicine Martin-Luther-University Halle-Wittenberg Halle Germany medical Data Science Faculty of Medicine Martin-Luther-University Halle-Wittenberg Halle Germany Institute for History and Ethics of Medicine Center for Health Sciences Halle Medical Faculty Martin-Luther-University Halle-Wittenberg Halle Germany Institute of General Practice and Family Medicine Center of Health Sciences Faculty of Medicine Martin-Luther-University Halle-Wittenberg Halle Germany

ISBN: (纸本)9781643682846

Background: Artificial intelligence (AI) in medicine is a very topical issue. As far as the attitudes and perspectives of the different stakeholders in healthcare are concerned, there is still much to be explored. Objective: Our aim was to determine attitudes and aspects towards acceptance of AI applications from the perspective of physicians in university hospitals. Methods: We conducted individual exploratory expert interviews. Low fidelity mockups were used to show interviewees potential application areas of AI in clinical care. Results: In principle, physicians are open to the use of AI in medical care. However, they are critical of some aspects such as data protection or the lack of explainability of the systems. Conclusion: Although some trends in attitudes e.g., on the challenges or benefits of using AI became clear, it is necessary to conduct further research as intended by the subsequent PEAK project. © 2022 European Federation for Medical informatics (EFMI) and IOS Press.

关键词： Health care

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Investigation and development of the workflow to clarify conditions of use for research data publishing in japan

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data science Journal 2020年第1期19卷 1-20页

作者： Minamiyama, Yasuyuki Ikeuchi, Ui Ueshima, Kunihiko Okayama, Nobuya Takeda, Hideaki Research Center for Open Science and Data Platforms National Institute of Informatics Tokyo Japan Department of English Language and Literature Faculty of Language and Literature Bunkyo University Saitama Japan Japan Data Exchange Inc Tokyo Japan Smart Infrastructure Consulting Department Hitachi Consulting Co. Ltd Tokyo Japan Principles of Informatics Research Division National Institute of Informatics Tokyo Japan

With the recent Open science movement and the rise of data-intensive science, many efforts are in progress to publish research data on the web. To reuse published research data in differ-ent fields, they must be made more generalized, interoperable, and machine-readable. Among the various issues related to data publishing, the conditions of use are directly related to their reuse potential. We show herein the types of external constraints and conditions of use in research data publishing in a Japanese context through the analysis of the interview and ques-tionnaire for practitioners. Although the conditions of research data use have been discussed only in terms of their legal constraints, we organize the inclusion of the non-legal constraints and data holders’ actual requirements. Furthermore, we develop practical guideline for exam-ining effective data publishing flow with licensing scenarios. This effort can be positioned to develop an infrastructure for data-intensive science, which will contribute to the realization of Open science. © 2020 The Author(s).

关键词： Open data

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Cooperative Visual Inertial Odometry for Heterogeneous Swarm of Drones Navigating in Noisy Environments

Cooperative Visual Inertial Odometry for Heterogeneous Swarm...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Kamil Canberk Atik Enes Erdogan Ahmet Yusuf Yahsi Feyzullah Kara Baris Yalcin Gü rkan Cetin Nazim Kemal Ure Faculty of Aeronautics and Astronautics ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Turkey Faculty of Computer and Informatics Engineering ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Turkey Faculty of Electrical and Electronics ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Turkey Havelsan Ankara Turkey

This paper presents a methodology for enhancing the localization performance of drone swarms by collaborative propagation of the estimates of drones equipped with higher cost sensors to the drones with lower-cost sensors. Although recent advances in visual-inertial odometry (VIO) enabled good performance in GPS denied environments, the localization performance is still highly dependent on the accuracy of inertial sensors. Moreover, environmental effects that corrupt visual inputs also contribute to the error rate of VIO estimates thus localization in noisy environments with low-grade sensors is still widely an open problem. We show that this problem can be alleviated when drones are navigating in the form of a swarm, and the relative distance measurements between drones are available through ultra-wideband (UWB) signals. In particular, we develop a collaborative Kalman filtering and optimization framework, where the drones with lower grade sensors receive updates from drones that have access to higher-quality measurements. We validate our approach in a simulation environment with realistic visual representations and show that the proposed methodology can significantly improve the localization performance, especially for the scenarios where the camera measurements are corrupted with high noise. We further present results in simplistic flight tests to demonstrate the applicability of our approach to real hardware.

关键词： Location awareness Visualization Costs Navigation Surveillance Teamwork Trajectory

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A new machine learning framework for understanding the link etween cannabis use and first-episode psychosis 12

A new machine learning framework for understanding the link ...

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12th Annual Conference on Health informatics Meets eHealth, eHealth 2018

作者： Alghamdi, Wajdi Stamate, Daniel Stahl, Daniel Zamyatin, Alexander Murray, Robin Di Forti, Marta Data Science and Soft Computing Lab Department of Computing Goldsmiths University of London United Kingdom Department of Biostatistics and Health Informatics Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom Faculty of Informatics Department of Applied Informatics National Research Tomsk State University Russia Department of Psychosis Studies Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom MRC Social Genetic and Developmental Psychiatry Centre Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom

ISBN: (纸本)9781614998570

Lately, several studies started to investigate the existence of links between cannabis use and psychotic disorders. This work proposes a refined Machine Learning framework for understanding the links between cannabis use and 1st episode psychosis. The novel framework concerns extracting predictive patterns from clinical data using optimised and post-processed models based on Gaussian Processes, Support Vector Machines, and Neural Networks algorithms. The cannabis use attributes' predictive power is investigated, and we demonstrate statistically and with ROC analysis that their presence in the dataset enhances the prediction performance of the models with respect to models built on data without these specific attributes. © 2018 The authors and IOS Press.

关键词： eHealth First-episode psychosis Gaussian processes Machine learning Neural networks Support vector machine

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Leveraging Users’ Trust and Reputation in Social Networks

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Procedia Computer science 2019年 162卷 955-962页

作者： Raquel Ureña Francisco Chiclana Ramon A. Carrasco Enrique Herrera-Viedma Institute of Artificial Intelligence (IAI) School of Computer Science and Informatics De Montfort University Leicester UK Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI) University of Granada Granada Spain Faculty of Statistics Complutense University of Madrid Madrid Spain

In on line communities, where there is a huge number of users that interact under anonymous identities, it has been observed that e-word of mouth is a very powerful influence tool. So far, this technology is well known in on-line marketplaces, such as Amazon 1 , eBay 2 or travel based platforms like Tripadvisor or Booking. However, these trust based approach can be leverage in other scenarios from e-democracy to trust based recommendations on e-health context and e-learning systems. The purpose of this contribution is to analyse the main existing trust and reputation mechanisms and to point out new research challenges that needs to be accomplished with the objective of fully exploiting these systems in real world on-line communities.

关键词： Trust reputation social networks influence group decision making e-word of mouth.

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Exploring heterogeneous market hypothesis using realized volatility

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AIP Conference Proceedings 2013年第1期1522卷 1138-1145页

作者： Wen Cheong Chin Zaidi Isa Abu Hassan Shaari Mohd Nor Faculty of Computing and Informatics Multimedia University 63100 Cyberjaya Selangor DE Malaysia Modeling and Data Analysis Center (DELTA) Faculty of Science and Technology Universiti Kebangsaan Malaysia 46300 Bangi Selangor DE Malaysia Faculty Economics and Management Universiti Kebangsaan Malaysia 46300 Bangi Selangor DE Malaysia

This study investigates the heterogeneous market hypothesis using high frequency data. The cascaded heterogeneous trading activities with different time durations are modelled by the heterogeneous autoregressive framework. The empirical study indicated the presence of long memory behaviour and predictability elements in the financial time series which supported heterogeneous market hypothesis. Besides the common sum-of-square intraday realized volatility, we also advocated two power variation realized volatilities in forecast evaluation and risk measurement in order to overcome the possible abrupt jumps during the credit crisis. Finally, the empirical results are used in determining the market risk using the value-at-risk approach. The findings of this study have implications for informationally market efficiency analysis, portfolio strategies and risk managements.

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