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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Bhandari, Shailendra Lencastre, Pedro Lind, Pedro Department of Computer Science OsloMet - Oslo Metropolitan University OsloN-0130 Norway Simula Research Laboratory Numerical Analysis and Scientific Computing Oslo0164 Norway

ISBN: (纸本)9798400704956

We explore the use of quantum generative adversarial networks QGANs for modeling eye movement velocity data. We assess whether the advanced computational capabilities of QGANs can enhance the modeling of complex stochastic distribution beyond the traditional mathematical models, particularly the Markov model. The findings indicate that while QGANs demonstrate potential in approximating complex distributions, the Markov model consistently outperforms in accurately replicating the real data distribution. This comparison underlines the challenges and avenues for refinement in time series data generation using quantum computing techniques. It emphasizes the need for further optimization of quantum models to better align with real-world data characteristics. © 2024 is held by the owner/author(s).

关键词： Generative adversarial networks

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ChronoEdgeMiner: A Novel Algorithm for Extracting Frequent Temporal Graphs from Data 23th

ChronoEdgeMiner: A Novel Algorithm for Extracting Frequent ...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Zmezm, Hareth Luna, Christian Luna, José María Ventura, Sebastián Department of Computer Science and Numerical Analysis University of Córdoba Córdoba14071 Spain University of Kerbala Karbala Iraq University of Córdoba Córdoba14071 Spain

ISBN: (纸本)9783031648496

The discovery of recurring event patterns is a cornerstone of advanced analytics. While basic insights can be derived from raw event sequences, the true potential is realized when time intervals between events are incorporated. This article proposes a methodology for extracting detailed event graphs (chronicles) from timestamped data, aiming to describe events in a factual and detailed form. Unlike conventional methods, these graphs provide a richer and more precise depiction of event sequences. The aim of this proposal is to extract chronicles in an efficient way. The experimental analysis, considering various databases, demonstrates the proposal’s notable superiority against state-of-the-art algorithms, particularly in runtime and memory requirements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Data mining

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A Non-Parametric Identification Scheme of SISO-LTI Systems Using Blaschke-Products∗ 32

A Non-Parametric Identification Scheme of SISO-LTI Systems U...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Ungvari, Gergo Soumelidis, Alexandros Dozsa, Tamas Eötvös Loránd University Faculty of Informatics Department of Numerical Analysis Budapest Hungary HUN-REN Institute for Computer Science and Control Systems and Control Laboratory Budapest Hungary

ISBN: (纸本)9798350395440

In this manuscript, a novel algorithm is presented for the identification of single input single output linear time invariant (SISO-LTI) systems. The proposed method is able to find poles of the transfer function describing the system without any assumptions on the system order. The results presented in this paper generalize earlier identification schemes based on expansions of the transfer function using generalized rational orthogonal basis functions (GOBFs). These previous methods depended on a convergent sequence constructed from the coefficients of the orthogonal GOBF expansion. The first contribution of this study is showing that a wider family of functions may be used to construct similar pole finding algorithms. The aforementioned previous approaches can be interpreted as special cases of the results presented here. In addition, using this generalization a new pole identification scheme is proposed using finite Blaschke-products. Through experiments, the practical utility of this scheme is verified when compared to earlier variations of the pole finding scheme. © 2024 IEEE.

关键词： Poles

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Evaluating Automatic Annotation Techniques for Fine-Tuning Large Language Models in Financial Sentiment analysis 4

Evaluating Automatic Annotation Techniques for Fine-Tuning L...

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4th International Conference on Sentiment analysis and Deep Learning, ICSADL 2025

作者： Youb, Ibtissam Hamlich, Mohamed Ventura, Sebastian Al-Obeidat, Feras University of Cordoba Department of Computer Science and Numerical Analysis Cordoba Spain University of Hassan Ii Ccps Laboratory Ensam Department of Electronic Casablanca Morocco College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331523923

Accurate sentiment analysis in financial contexts is crucial for market analysis and investment decisions. However, manually annotating large datasets for training models is time-consuming and costly. This study evaluates two automatic an-notation tools, VADER and TextBlob, for their effectiveness in generating labels for training large language models (LLMs) like BERT and GPT-2 in financial sentiment analysis. We used a large dataset of stock market-related tweets, including a manually annotated subset for benchmarking. The remaining tweets were automatically labeled using VADER and TextBlob. We trained BERT and GPT-2 models with these sentiment labels and compared their performance against the benchmark dataset. Our findings show that models trained with VADER annotations had a higher correlation with human-labeled data (62% accuracy) compared to those trained with TextBlob annotations (48% accuracy). These results suggest that VADER is more suitable for automatic annotation in financial sentiment analysis, providing more accurate and reliable sentiment labels, which can significantly improve the efficiency of training large language models for financial applications. © 2025 IEEE.

关键词： Tweets

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Interactive Multi-Objective Evolutionary Optimization of Software Architectures

arXiv

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arXiv 2024年

作者： Ramírez, Aurora Romero, José Raúl Ventura, Sebastián Department of Computer Science and Numerical Analysis University of Córdoba Córdoba14071 Spain

While working on a software specification, designers usually need to evaluate different architectural alternatives to be sure that quality criteria are met. Even when these quality aspects could be expressed in terms of multiple software metrics, other qualitative factors cannot be numerically measured, but they are extracted from the engineer’s know-how and prior experiences. In fact, detecting not only strong but also weak points in the different solutions seems to fit better with the way humans make their decisions. Putting the human in the loop brings new challenges to the search-based software engineering field, especially for those human-centered activities within the early analysis phase. This paper explores how the interactive evolutionary computation can serve as a basis for integrating the human’s judgment into the search process. An interactive approach is proposed to discover software architectures, in which both quantitative and qualitative criteria are applied to guide a multi-objective evolutionary algorithm. The obtained feedback is incorporated into the fitness function using architectural preferences allowing the algorithm to discern between promising and poor solutions. Experimentation with real users has revealed that the proposed interaction mechanism can effectively guide the search towards those regions of the search space that are of real interest to the *** Codes 68 © 2024, CC BY.

关键词： Software design

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Barycentre Averaging for the Move-Split-Merge Time Series Distance Measure 15

Barycentre Averaging for the Move-Split-Merge Time Series Di...

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15th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2023 as part of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023

作者： Holder, Christopher Guijo-Rubio, David Bagnall, Anthony School of Computing Sciences University of East Anglia Norwich United Kingdom Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain School of Electronics and Computer Science University of Southampton Southampton United Kingdom

ISBN: (纸本)9789897586712

Distance functions play a core role in many time series machine learning algorithms for tasks such as clustering, classification and regression. Time series often require bespoke distance functions because small offsets in time can lead to large distances between series that are conceptually similar. Elastic distances compensate for misalignment by creating a path through a cost matrix by warping and/or editing time series. Time series are most commonly clustered with partitional algorithms such as k-means and k-medoids using elastic distance measures such as Dynamic Time Warping (DTW). The distance is used to assign cases to the closest cluster representative. k-means requires the averaging of time series to find these representative centroids. If DTW is used to assign membership, but the arithmetic mean is used to find centroids, k-means performance degrades significantly. An averaging technique specific to DTW, called DTW Barycentre Averaging (DBA), overcomes the averaging problem however, can only be used with DTW. As such alternative distance functions such as Move-Split-Merge (MSM) are forced to use the arithmetic mean to compute new centroids and suffer similar degraded performance as k-means-DTW without DBA. To address this we propose a averaging method for MSM distance, MSM Barycentre Averaging (MBA) and show that when used to find centroids it significantly improves MSM based k-means and is better than commonly used alternatives. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Time series

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Cross-Modality Gait Recognition: Bridging LiDAR and Camera Modalities for Human Identification 18

Cross-Modality Gait Recognition: Bridging LiDAR and Camera M...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Wang, Rui Shen, Chuanfu Marin-Jimenez, Manuel J. Huang, George Q. Yu, Shiqi Southern University of Science and Technology Department of Computer Science and Engineering China The University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Hong Kong University of Cordoba Department of Computing and Numerical Analysis Spain The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong

ISBN: (纸本)9798350364132

Current gait recognition research mainly focuses on identifying pedestrians captured by the same type of sensor, neglecting the fact that individuals may be captured by different sensors in order to adapt to various environments. A more practical approach should involve cross-modality matching across different sensors. Hence, this paper focuses on investigating the problem of cross-modality gait recognition, with the objective of accurately identifying pedestrians across diverse vision sensors. We present CrossGait inspired by the feature alignment strategy, capable of cross retrieving diverse data modalities. Specifically, we investigate the cross-modality recognition task by initially extracting features within each modality and subsequently aligning these features across modalities. To further enhance the cross-modality performance, we propose a Prototypical Modality-shared Attention Module that learns modality-shared features from two modality-specific features. Additionally, we design a Cross-modality Feature Adapter that transforms the learned modality-specific features into a unified feature space. Extensive experiments conducted on the SUSTech1K dataset demonstrate the effectiveness of CrossGait: (1) it exhibits promising cross-modality ability in retrieving pedestrians across various modalities from different sensors in diverse scenes, and (2) CrossGait not only learns modality-shared features for cross-modality gait recognition but also maintains modality-specific features for single-modality recognition. © 2024 IEEE.

关键词： Unattended sensors

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JCLEC-MO: a Java suite for solving many-objective optimization engineering problems

arXiv

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arXiv 2024年

作者： Ramírez, Aurora Romero, José Raúl García-Martínez, Carlos Ventura, Sebastián Department of Computer Science and Numerical Analysis University of Córdoba Córdoba14071 Spain

Although metaheuristics have been widely recognized as efficient techniques to solve real-world optimization problems, implementing them from scratch remains difficult for domain-specific experts without programming skills. In this scenario, metaheuristic optimization frameworks are a practical alternative as they provide a variety of algorithms composed of customized elements, as well as experimental support. Recently, many engineering problems require to optimize multiple or even many objectives, increasing the interest in appropriate metaheuristic algorithms and frameworks that might integrate new specific requirements while maintaining the generality and reusability principles they were conceived for. Based on this idea, this paper introduces JCLEC-MO, a Java framework for both multi- and many-objective optimization that enables engineers to apply, or adapt, a great number of multiobjective algorithms with little coding effort. A case study is developed and explained to show how JCLEC-MO can be used to address many-objective engineering problems, often requiring the inclusion of domain-specific elements, and to analyze experimental outcomes by means of conveniently connected R *** Codes 68T20 © 2024, CC BY.

关键词： Reusability

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MIML library: a Modular and Flexible Library for Multi-instance Multi-label Learning

arXiv

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arXiv 2024年

作者： Belmonte, Álvaro Zafra, Amelia Gibaja, Eva Department of Computer Science and Numerical Analysis University of Córdoba Spain University of Córdoba Spain

MIML library is a Java software tool to develop, test, and compare classification algorithms for multi-instance multi-label (MIML) learning. The library includes 43 algorithms and provides a specific format and facilities for data managing and partitioning, holdout and cross-validation methods, standard metrics for performance evaluation, and generation of reports. In addition, algorithms can be executed through xml configuration files without needing to program. It is platform-independent, extensible, free, open-source, and available on GitHub under the GNU General Public License. © 2024, CC BY.

关键词： Open source software

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Sequential Solution Strategies for the Cahn-Hilliard-Biot Model

Sequential Solution Strategies for the Cahn-Hilliard-Biot ...

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European Conference on numerical Mathematics and Advanced Applications, ENUMATH 2023

作者： Storvik, Erlend Riethmüller, Cedric Both, Jakub W. Radu, Florin Adrian Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Institute of Applied Analysis and Numerical Simulation University of Stuttgart Stuttgart Germany Centre for Modeling of Coupled Subsurface Dynamics Department of Mathematics University of Bergen Bergen Norway

ISBN: (纸本)9783031861680

This paper presents a study of solution strategies for the Cahn-Hilliard-Biot equations, a complex mathematical model for understanding flow in deformable porous media with changing solid phases. Solving the Cahn-Hilliard-Biot system poses significant challenges due to its coupled, nonlinear and non-convex nature. We explore various solution algorithms, comparing monolithic and splitting strategies, focusing on both their computational efficiency and robustness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computational efficiency

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