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Findings of the Second Shared Task on Multilingual Coreference Resolution 6

Findings of the Second Shared Task on Multilingual Coreferen...

CRAC Shared Task on Multilingual Coreference Resolution at 6th Workshop on Computational Models of Reference, Anaphora and Coreference, CRAC 2023

作者： Žabokrtský, Zdeněk Konopík, Miloslav Nedoluzhko, Anna Novák, Michal Ogrodniczuk, Maciej Popel, Martin Pražák, Ondřej Sido, Jakub Zeman, Daniel Charles University Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Prague Czech Republic University of West Bohemia Faculty of Applied Sciences Department of Computer Science and Engineering Pilsen Czech Republic Institute of Computer Science Polish Academy of Sciences Warsaw Poland

ISBN: (纸本)9781955917025

This paper summarizes the second edition of the shared task on multilingual coreference resolution, held with the CRAC 2023 workshop. Just like last year, participants of the shared task were to create trainable systems that detect mentions and group them based on identity coreference;however, this year’s edition uses a slightly different primary evaluation score, and is also broader in terms of covered languages: version 1.1 of the multilingual collection of harmonized coreference resources CorefUD was used as the source of training and evaluation data this time, with 17 datasets for 12 languages. 7 systems competed in this shared task. © CRAC 2023. All Rights Reserved.

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Electron- Phonon Interaction in Open GaAs/AIAs Nanosystems: A New Method 42

Electron- Phonon Interaction in Open GaAs/AIAs Nanosystems: ...

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42nd IEEE International Conference on Electronics and Nanotechnology, ELNANO 2024

作者： Boyko, Igor Seti, Julia Tkach, Mykola Ternopil Ivan Puluj National Technical University Department of Software Engineering Ternopil Ukraine Yuriy Fedkovych Chernivtsi National University Department of Information Technology and Computer Physics Chernivtsy Ukraine Lviv Polytechnic National University Department of Applied Mathematics Lviv Ukraine

ISBN: (纸本)9798350368178

A new theoretical method for describing electron-phonon interaction in open nanosystems created on the basis of gallium arsenide and aluminium arsenide alloys in the presence of a time-dependent electromagnetic field is proposed. The results obtained on the basis of the developed method are compared with the results obtained on the basis of the classical model of closed nanosystems. The conditions and configurations of the nanosystems under study for which the results of both methods coincide have been established by the direct calculations. © 2024 IEEE.

关键词： Gallium arsenide

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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: applied mathematics and Physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Radiation effects

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DeepONet: Learning the sound propagation in 1D with parameterized sources using deep learning for approximating the wave equation operators 24

DeepONet: Learning the sound propagation in 1D with paramete...

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24th International Congress on Acoustics, ICA 2022

作者： Borrel-Jensen, Nikolas Engsig-Karup, Allan P. Jeong, Cheol-Ho Technical University of Denmark Department of Electrical and Photonics Engineering Denmark Technical University of Denmark Department of Applied Mathematics and Computer Science Denmark

Realistic acoustics for virtual environments – such as computer games and mixed reality – is essential to achieve an immersive user experience and is getting more attention these years. Numerical methods have traditionally been used for calculating the sound propagation off-line;however, when solving for moving sources, these methods quickly get intractable both from a calculation time and storage viewpoint. Therefore, we propose a novel approach, where the wave equation operators (contrary to functions) are learned for arbitrary source inputs. The DeepONet architecture is used, as it is a general framework having already demonstrated its capability to approximate operators in other applications. As a result, an efficient surrogate model handling impedance boundaries in 1-D is learned. We present results of a model showing errors below 2%, executes in milliseconds and can be trained in less than 5 minutes, leaving optimistic expectations for extending the method to more complex 3-D scenes. © 2022 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： computer games

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Cyber Deception System based on Monte Carlo Simulation in the Mobile Edge Computing (MEC)

Cyber Deception System based on Monte Carlo Simulation in th...

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2023 IEEE International Conference on computer, Information, and Telecommunication Systems, CITS 2023

作者： Kabdjou, Joelle Tagne, Elie Fute Rawat, Danda B. Acosta, Jaime Kamhoua, Charles University of Dschang Department of Mathematics and Computer Science Dschang Cameroon University of Buea Department of Computer Engineering Dschang Buea Cameroon Howard University Department of Electrical Engineering and Computer Science Washington United States Network Security Branch Devcom Army Research Laboratory Adelphi United States

ISBN: (纸本)9798350336092

The frequency of malicious activities by cyber attackers is on the rise, posing a significant challenge to counter cyber attacks in the Mobile Edge Computing (MEC) environment. The vulnerability of wireless networks, which serve as the primary communication media for MEC servers and IoT applications, is due to their open nature of communication. This work proposes a MEC architecture that utilizes Network Function Virtualization (NFV) and Service Function Chain (SFC) to combat cyber adversaries. Additionally, we evaluate the performance of a cyber deception system, which leverages a software-defined networking (SDN) controller and decision- maker to continuously monitor incoming MEC traffic and direct cyber adversaries to a deception MEC server. The deception MEC server collects information about cyber adversaries' intent, capabilities, and potential damage to the system, thus securing legitimate users from cyber attacks. The proposed approach was evaluated using Monte Carlo simulations, and numerous results were obtained which demonstrate the effectiveness of cyber deception in MEC. © 2023 IEEE.

关键词： Network function virtualization

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Pied kingfisher optimizer: a new bio-inspired algorithm for solving numerical optimization and industrial engineering problems

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Neural Computing and Applications 2024年第25期36卷 15455-15513页

作者： Bouaouda, Anas Hashim, Fatma A. Sayouti, Yassine Hussien, Abdelazim G. LSIB Laboratory FST Mohammedia Hassan II University of Casablanca Mohammedia Morocco Faculty of Engineering Helwan University Cairo Egypt MEU Research Unit Middle East University Amman11831 Jordan Department of Computer and Information Science Linköping University Linköping Sweden Faculty of Science Fayoum University Faiyum Egypt Applied Science Research Center Applied Science Private University Amman Jordan

In this study, we introduce the pied kingfisher optimizer (PKO), a novel swarm-based meta-heuristic algorithm that draws inspiration from the distinctive hunting behavior and symbiotic relationships observed in pied kingfishers in the natural world. The PKO algorithm is structured around three distinct phases: perching/hovering for prey (exploration/diversification), diving for prey (exploitation/intensification), and fostering symbiotic relations. These behavioral aspects are translated into mathematical models capable of effectively addressing a wide array of optimization challenges across diverse search spaces. The algorithm’s performance is rigorously evaluated across thirty-nine test functions, which encompass various unimodal, multimodal, composite, and hybrid ones. Additionally, eight real-world engineering optimization problems, including both constrained and unconstrained scenarios, are considered in the assessment. To gauge PKO’s efficacy, it is subjected to a comparative analysis against 3 categories of rival optimizers. The 1st category comprises well-established and widely-cited optimizers such as particle swarm optimization and genetic algorithm. The 2nd category encompasses recently published algorithms, including Harris Hawks optimization, Whale optimization algorithm, sine cosine algorithm, Grey Wolf optimizer, gravitational search algorithm, and moth-flame optimization. The 3rd category includes advanced algorithms, such as covariance matrix adaptation evolution strategy and Ensemble Sinusoidal Differential Covariance Matrix Adaptation with Euclidean Neighborhood (LSHADE-cnEpSin). The comparative analysis employs various performance metrics, including the Friedman mean rank and the Wilcoxon rank-sum test, to reveal PKO’s effectiveness and efficiency. The overall results highlight PKO’s exceptional ability to tackle intricate optimization problems characterized by challenging search spaces. PKO demonstrates superior exploration and exploitation tend

关键词： Global optimization

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A Novel Approach for Detection of Soybean Leaf Disease Using Bayesian Optimized-KNN Classifier with NCA-Based Feature Selection 3rd

A Novel Approach for Detection of Soybean Leaf Disease Using...

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3rd International Conference on "Emerging Trends and Technologies on Intelligent Systems" (ETTIS-2023)

作者： Mahobiya, Chandrakant Iyer, Sailesh Kolhe, Savita Department of Computer Science and Engineering School of Engineering and Applied Sciences Rai University Gujarat Ahmedabad India ICAR Indian Institute of Soybean Research Madhya Pradesh Indore India

ISBN: (纸本)9789819939626

Agriculture serves as the backbone of India's economy, with approximately 72% of India’s population relying on agriculture as an important source of income. Every year huge losses on crops are identified due to plant disease. It is required to detect plant disease to save crops and minimize use of pesticides. This paper proposed novel NCA feature selection-based optimized KNN classifier for soybean plant disease detection, while many research work has been done on soybean data set to detect plant leaf disease. For comparison of our novel work, we also implemented standard KNN and proposed Bayesian optimized-KNN classifier. Proposed work is compared with existing ANN and SVM classifier method. In this work, soybean data set obtained from UCI machine learning repository is used. The soybean dataset is disproportionate and contained 19 classes out of which we used suing only 15 classes. The main focus of this work is to find novel approach which will accurately detect disease. Our research works shows that NCA feature selection-based optimized detect disease more accurately as compared to ANN, SVM, and KNN. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crops

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Leveraging Smote and Random Forest for Improved Credit Card Fraud Detection 5

Leveraging Smote and Random Forest for Improved Credit Card ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Ruchita, Maddala Bhargavi, Maridu Rakshita, Maddala Nandini, Bellamkonda Chaitanya Aziz, Irfan Gopi, Jujjuri Vignan's Foundation for Science Technology and Research Department of Computer Science and Engineering Vadlamudi Andhra Pradesh Guntur India Acharya Nagarjuna University Department of Mathematics Nagarjuna Nagar Andhra Pradesh Guntur India

ISBN: (纸本)9798331530013

The accelerating speed of digital transactions has made credit card theft a serious concern to customers and banks alike. Traditional means of fraud detection are no longer as strong as today's more sophisticated methods. This paper presents a summary of various machine learning techniques applied in credit card fraud detection. Several algorithms are highlighted, including logistic regression, random forests, and boosting methods such as gradient boosting and LightGBM. These algorithms were selected due to their capacity to manage big datasets and identify patterns unique to fraud. According to the results, the best performance was obtained by random forests, which achieved 99.30% accuracy, outperforming all other methods. Gradient boosting and logistic regression were also competitive, reaching 98.5% accuracy. This study offers recommendations for improving fraud detection techniques while showcasing the efficacy of machine learning in this regard. © 2024 IEEE.

关键词： Adversarial machine learning

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Constructing the ChatGPT for PDF Files with Langchain - AI 7

Constructing the ChatGPT for PDF Files with Langchain - AI

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Prem Jacob, T. Bizotto, Beatriz Lucia Salvador Sathiyanarayanan, Mithileysh Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India University Center Unifacvest Department of Social And Applied Sciences Lages Brazil Research & Innovation MIT Square London United Kingdom

ISBN: (纸本)9798350359299

Queries in PDFs can be time-consuming and labor-intensive because of the unstructured nature of the PDF document type and the need for accurate and relevant search results. By applying cutting-edge algorithms for natural language processing to examine PDF documents and extract relevant data, LangChain solves these difficulties. It makes use of an easy search interface, adjustable filters, and efficient indexing and retrieval mechanisms to enhance the search experience. To efficiently retrieve relevant information from PDF documents, users can annotate critical portions, store queries, and create bookmarks with LangChain. The characteristics of LangChain improve overall productivity and greatly simplify PDF querying. Semantic search, driven by the latest Transformer language models, represents a significant evolution in information retrieval systems. This research work explores the capabilities of semantic search to efficiently retrieve documents from large collections in response to natural language queries. Unlike traditional keyword-based approaches, semantic search connects the power of Transformer models to discern meaning, providing users with more contextually relevant and accurate results within seconds. This technology not only enhances the user experience by delivering superior matches from document collections but also lays the foundation for tackling more intricate tasks, like text summarization and question-answering. The research investigates the impact of semantic search on information retrieval efficiency and accuracy, comparing its performance with conventional methods. The findings presented herein not only showcase the immediate benefits of semantic search but also open paths for future research and development in natural language processing and its applications. © 2024 IEEE.

关键词： Information retrieval systems

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A Bicriterion Concentration Inequality and Prophet Inequalities for k-Fold Matroid Unions 16

A Bicriterion Concentration Inequality and Prophet Inequalit...

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16th Innovations in Theoretical computer Science Conference, ITCS 2025

作者： Alon, Noga Gravin, Nick Pollner, Tristan Rubinstein, Aviad Wang, Hongao Weinberg, S. Matthew Zhang, Qianfan Department of Mathematics Princeton University NJ United States Schools of Mathematics and Computer Science Tel Aviv University Tel Aviv Israel Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics China Department of Management Science and Engineering Stanford University CA United States Department of Computer Science Stanford University CA United States Department of Computer Science Purdue University West LafayetteIN United States Department of Computer Science Princeton University NJ United States

ISBN: (纸本)9783959773614

We investigate prophet inequalities with competitive ratios approaching 1, seeking to generalize k-uniform matroids. We first show that large girth does not suffice: for all k, there exists a matroid of girth ≥ k and a prophet inequality instance on that matroid whose optimal competitive ratio is12. Next, we show k-fold matroid unions do suffice: we provide a prophet inequality with competitive ratio (Formula presented) for any k-fold matroid union. Our prophet inequality follows from an online contention resolution scheme. The key technical ingredient in our online contention resolution scheme is a novel bicriterion concentration inequality for arbitrary monotone 1-Lipschitz functions over independent items which may be of independent interest. applied to our particular setting, our bicriterion concentration inequality yields "Chernoff-strength" concentration for a 1-Lipschitz function that is not (approximately) self-bounding. © Noga Alon, Nick Gravin, Tristan Pollner, Aviad Rubinstein, Hongao Wang, S. Matthew Weinberg, and Qianfan Zhang.

关键词： Choquet integral

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