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Impact of Market Design and Trading Network Structure on Market Efficiency 1

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19th International Workshop on Modelling and Mining Networks (WAW)

作者： Arnosti, Nick Kaminski, Bogumil Pralat, Pawel Zawisza, Mateusz Univ Minnesota Dept Ind & Syst Engn 207 Church St SE Minneapolis MN 55455 USA SGH Warsaw Sch Econ Decis Anal & Support Unit Niepodleglosci 162 PL-02554 Warsaw Poland Toronto Metropolitan Univ Dept Math 350 Victoria St Toronto ON Canada

ISBN: (数字)9783031592058

ISBN: (纸本)9783031592041;9783031592058

This paper investigates the influence of market design, market size, and trading network structure on market efficiency and trade participation rate. The study considers two market designs: Zero Intelligence Traders (ZIT) in Chamberlin's bilateral haggling market and a greedy matching of traders on a network. Sellers and buyers are embedded in a random bipartite graph with varying network densities, and markets vary in size from 20 to 2000 traders. Simulations reveal that greedy matching generally leads to more efficient allocations than ZIT trading networks. By increasing the average degree of a trading network from 1 to 5 or 10, market efficiency can be significantly improved for both market designs, achieving 89% and 95% of maximum efficiency, respectively. The study also contradicts the common belief that larger markets are better, as no significant impact of market size was found. We discuss the policy implications of these results.

关键词： Zero-Intelligence Traders bilateral exchange market efficiency Hungarian Algorithm greedy matching algorithm network science agent-based simulation (ABM) bipartite graph

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A Simulator for Minero-Petrographic and Chemical Research Instruments 1

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26th International Conference on Pattern Recognition, ICPR 2022

作者： Sinitò, Diego Verderame, Alessandro Lombardo, Carmelo Treffiletti, Alessandro Fugazzotto, Maura Allegra, Dario Santarcangelo, Vito Stanco, Filippo Barone, Germana Department of Mathematics and Computer Science University of Catania Catania Italy iInformatica S.r.l. Trapani Italy Department of Biological Geological and Environmental Science University of Catania Catania Italy

ISBN: (数字)9783031377310

ISBN: (纸本)9783031377303

In recent years, digital technologies and serious game have widely affected the cultural heritage sector, offering incredible opportunities to enhance the experiential value of heritage assets and improve cultural activities. Due to pandemic and the closure of museums the demand for new digital remote solutions for Cultural Heritage has seen a great increase. Thanks to a collaboration between University of Catania, the Museum of Mineralogy, Petrography and Volcanology and a local SME iInformatica S.r.l a serious game for dissemination of mineralogic and petrographic science was developed. The idea behind the project is to give to all the possibility to learn by doing. Through the simulator, users can assume the role of researchers in mineralogy and petrography and, thus, study the minerals constituting the rocks. Within the game a player can learn how to use a petrographic microscope and how to recognise a rock by analysing the single minerals that constitute it and, therefore, to identify the rock itself. © 2023, Springer Nature Switzerland AG.

关键词： Serious games

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Defending Against Poisoning Attacks in Federated Prototype Learning on Non-IID Data 18th

Defending Against Poisoning Attacks in Federated Prototype ...

引用

18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Jie Zhang, Hongliang Wang, Guijuan Dong, Anming Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China

ISBN: (纸本)9783031714665

Federated learning (FL) is an emerging distributed machine learning paradigm that enables participants to cooperatively train learning tasks without revealing the raw data. However, the distributed nature of FL makes it susceptible to poisoning attacks, especially when the local data of participants are non-independent and identically diatributed (non-IID). Although several defense methods have been proposed to mitigate poisoning attacks, their effectiveness is limited by the specific assumptions about the data distribution. In this work, we propose a new defense strategy, FedAPA (Federated Prototype Learning Against Poisoning Attacks). Specifically, we use abstract class prototypes to communicate between the clients and server, thus effectively alleviating the impact of non-IID data. Moreover, we propose a new abnormal client detection method that aims to mitigate the impact of malicious clients while distinguishing between malicious and benign clients, thereby effectively defending against poisoning attacks. Extensive experiments on different datasets show that FedAPA can effectively resist the poisoning attacks under various data distributions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Estimating Soil Organic Carbon from Multispectral Images Using Physics-Informed Neural Networks 17th

Estimating Soil Organic Carbon from Multispectral Images Us...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Sargeant, James Wei Teng, Shyh Murshed, Manzur Paul, Manoranjan Brennan, David Institute of Innovation Science and Sustainability Federation University Ballarat Australia School of Information Technology Deakin University Burwood Australia School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW2795 Australia Wimmera Catchment Management Authority Horsham Australia

ISBN: (纸本)9789819609628

Understanding the amount of Soil Organic Carbon (SOC) at farm and field scale is a necessary precursor to effective management, important for both agricultural productivity and to reduce CO2 emissions. To avoid the prohibitive cost of measurement, SOC can be estimated by using multispectral images. In this study, we propose a novel Physics-Informed Convolutional Neural Network (CNN) to model well-known but noisy relationship between a soil index and SOC using the network’s loss function. This study is also conducted by resampling the European Land Use/Classification Area Survey (LUCAS) dataset to Sentinel-2 bands. Our experimental results show that our proposed network converges more quickly, has a lower root mean squared error (RMSE) and is more robust (as measured by the standard deviation of RMSE over multiple trials) than a compatible standard CNN. The operation of the novel Physics-Informed CNN is explained in terms of the components of the loss function. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Carbon sequestration

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Experimental Validation of an Interface for a Human-Robot Interaction Within a Collaborative Task 1

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Proceedings of the 8th International Conference on Interactive Collaborative Robotics, ICR 2023

作者： Mustafin, Maksim Chebotareva, Elvira Li, Hongbing Magid, Evgeni Intelligent Robotics Department Institute of Information Technology and Intelligent Systems Kazan Federal University 35 Kremlyovskaya st. Kazan420111 Russia Department of Instrument Science and Engineering Shanghai Jiao Tong University Minhang Shanghai200240 China Tikhonov Moscow Institute of Electronics and Mathematics HSE-University 34 Tallinn st. Moscow123458 Russia

ISBN: (数字)9783031431111

ISBN: (纸本)9783031431104

This paper presents a prototype of a non-contact UR robot based Virtual Control (UR-VC) system for collaborative robots of the UR family, which is based on computer vision techniques and a virtual interaction interface. A control method involved specific hand movements within a field of view of a web camera, which was connected to a laptop with the running UR-VC system. We present the UR-VC system and the results of an experimental validation. To inquire if the UR-VC system is comfortable and user-friendly for an interaction with collaborative robots and to study opportunities for a further development and expansion directions of the system, we designed a test case that simulates a joint product assembly in a collaborative workspace. The constructed collaborative workspace included the UR3e robot, the laptop with the running UR-VC system and assembly parts for a collaborative task. 24 participants were involved in the experiments. First, the participants learned how to control the robot using the UR-VC system. After the training, all participants successfully controlled the robot using the proposed interface for performing the collaborative task. Participants’ experience of operating the robot was analyzed via surveys, their unconstrained comments and video recordings of the experiments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Virtual reality

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Active Nodes Maximization in a Virus Spread Model: An SI2R Malware Propagation Model 11th

Active Nodes Maximization in a Virus Spread Model: An SI2R M...

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11th International Conference on Network Games, Control and Optimization

作者： Ngoufo, Arthur Kouam, Willie Hayel, Yezekael Deugoue, Gabriel Kamhoua, Charles Univ Dschang Dschang Cameroon Avignon Univ CERI LIA Avignon France DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9783031785993;9783031786006

The threat of malware is increasing and poses significant computer security risks to both individuals and organizations. Understanding the tactics employed by these malicious software entities and their dynamics during the epidemic process is crucial for designing robust defense strategies and ensuring the protection of computer systems. In this article, we investigate a population of digital nodes (such as phones, computers,...) under attack by modeling the network using a susceptible-infected-resistant (SI2R) compartmental model, where hosts can transition between susceptible, infected, or resistant states. Our model considers two types of infected nodes: active nodes, whose resources are exploited, and passive nodes, which spread the virus. Both active and passive nodes can develop resistance with certain probabilities, which are influenced by the resource utilization percentages set by the malware designer. Thus, rather than optimizing resource utilization, the malware's goal is to maximize the number of active hosts at the end of the process. To achieve this objective, we aim to determine the optimal percentage of passive nodes to consider at each period, recognizing that the variation in the number of active nodes depends on the number of passive nodes.

关键词： computer networks Optimal control Epidemic model Two timescale method SPSA method

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Improving Low-Resource NMT with Parser Generated Syntactic Phrases 1

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20th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2019

作者： Gupta, Kamal Kumar Sen, Sukanta Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna Patna India

ISBN: (数字)9783031243370

ISBN: (纸本)9783031243363

Recently, neural machine translation (NMT) has become highly successful achieving state-of-the-art results on many resource-rich language pairs. However, it fails when there is a lack of sufficiently large amount of parallel corpora for a domain and/or language pair. In this paper, we propose an effective method for NMT under a low-resource scenario. The model operates by augmenting the original training data with the examples extracted from the parse trees of the target-side sentences. It provides important evidences to the model as these phrases are relatively smaller and linguistically correct. Our experiment on the benchmark WMT14 dataset shows an improvement of 3.28 BLEU and 3.41 METEOR score for Hindi to English translation. Evaluation on the same language pair with relatively much smaller datasets of judicial and health domains also show the similar trends with significant performance improvement in terms of BLEU (15.63 for judicial and 15.97 for health) and METEOR (14.30 for judicial and 15.93 for health). © 2023, Springer Nature Switzerland AG.

关键词： Computational linguistics

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Language Models for Automatic Distribution of Review notes in Movie Production 24th

Language Models for Automatic Distribution of Review Notes...

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24th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023

作者： Garcés, Diego Santos, Matilde Fernández-Llorca, David Computer Science Faculty Complutense University of Madrid Madrid Spain Skydance Animation Madrid Madrid Spain Institute of Knowledge Technology Complutense University of Madrid Madrid Spain Computer Engineering Department University of Alcalá de Henares Madrid Spain European Commission Joint Research Centre Seville Spain

ISBN: (纸本)9783031482311

During the several years of production of an animated movie, review meetings take place daily, where supervisors and directors generate text notes about fixes needed for the movie. These notes are manually assigned to artistic departments for them to fixed. Being manual, many notes are not properly assigned and are never fixed, lowering the quality of the final movie. This paper presents a proposal for automating the distribution of these notes using multi-label text classification techniques. The comparison of the results obtained by fine-tuning several transformer-based language models is presented. A highest mean accuracy of 0.776 is achieved assigning several departments to each of the review notes in the test set with a BERT Multilingual model. A mean accuracy of 0.762 was reached in just 10 epochs and 10 min of training on an RTX-3090 with a DistilBERT transformer model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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Differential Characteristics and Collaborative Interactions of Institutional and Personal Twitter Accounts in a Citizen science Context 29th

Differential Characteristics and Collaborative Interactions ...

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29th International Conference on Collaboration Technologies and Social Computing (CollabTech)

作者： Krukowski, Simon Martinez-Martinez, Fernando Hoppe, H. Ulrich Univ Duisburg Essen Duisburg Germany Univ Rey Juan Carlos Mostoles Spain RIAS Inst Duisburg Germany

ISBN: (纸本)9783031421402;9783031421419

The analysis of Twitter data can help to better understand the interplay between scientific institutions, volunteers and other actors in a Citizen science (CS) context. A first essential distinction has to be made between different user types such as organizations and individuals. We have applied and evaluated different machine learning approaches for this purpose. On this basis, we have analyzed networks based on different Twitter relations to characterize roles and interactions between different user types. Relations based on retweets, quotes, and replies capture the short term dynamics of on-going discussions. Our findings indicate that institutions are the main information sources, whereas personal users have an important role in active information spreading and dissemination through retweeting and quoting. Projecting the dynamic interactions onto a static network based on the follower relationship shows that pathways of dynamic information diffusion are largely determined by the static follower topology. These findings provide strategic information for managing CS-related discussions.

关键词： Twitter user classification Citizen science participation profiles network measures of collaboration

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Practical and Ethical Considerations for Generative AI in Medical Imaging 2nd

Practical and Ethical Considerations for Generative AI in...

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2nd International Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and 3rd International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with the International Conference on Medical Image Computing and computer Assisted Interventions, MICCAI 2024

作者： Jha, Debesh Rauniyar, Ashish Hagos, Desta Haileselassie Sharma, Vanshali Tomar, Nikhil Kumar Zhang, Zheyuan Isler, Ilkin Durak, Gorkem Wallace, Michael Yazici, Cemal Berzin, Tyler Biswas, Koushik Bagci, Ulas Machine and Hybrid Intelligence Laboratory Northwestern University Evanston United States SINTEF Digital Trondheim Norway Department of Electrical Engineering and Computer Science Howard University WashingtonDC United States Department of Computer Science University of Central Florida Orlando United States Mayo Clinic Florida Jacksonville United States Department of Medicine University of Chicago Chicago United States Beth Israel Deaconess Medical Center Harvard Medical School Boston United States

ISBN: (纸本)9783031727863

Generative Artificial Intelligence (AI) has the potential to transform medicine. It is helpful to clinicians and radiologists for diagnosis, screening, treatment planning, interventions, and drug development. It benefits the clinical flow with real-time decision-support systems. While generative AI can potentially improve healthcare, it also introduces new ethical issues that require careful analysis and mitigation strategies. This work emphasizes the ethical aspects of generative AI in medical imaging, aiming to ensure that advancements in this field align with established ethical principles and societal values. We delve into the ethical implications surrounding bias, fairness, patient privacy, consent, transparency, explainability, intellectual property, and data ownership. Furthermore, we discuss regulations governing the use of synthetic medical data. To promote equitable application of these powerful tools, we also propose clear guidelines for promoting fairness, mitigating bias, and ensuring diversity within generative AI models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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