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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Malina, Andra Babeş-Bolyai University Tiberiu Popoviciu Institute of Numerical Analysis Romanian Academy Faculty of Mathematics and Computer Science Cluj-Napoca Romania

ISBN: (纸本)9798331532833

We propose an application of the Shepard operator combined with two radial basis functions in image processing. This method aims to reconstruct damaged black-and-white or color images, considering both a global and a local approach. In the construction of the Shepard operator, we use the inverse quadratic and the inverse multiquadric radial basis functions. © 2024 IEEE.

关键词： Image reconstruction

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Zero-Vuln: Using deep learning and zero-shot learning techniques to detect zero-day Android malware

Zero-Vuln: Using deep learning and zero-shot learning techni...

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2023 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Sawadogo, Zakaria Dembele, Jean-Marie Mendy, Gervais Ouya, Samuel Gaston Berger University Laboratory of Numerical Analysis and Computer Science Dakar Senegal Cheikh Anta Diop University Computer Science Telecommunications and Applications Dakar Senegal

ISBN: (纸本)9798350322972

The prevalence of cyber security threats, such as the Android Zero-day vulnerability, is becoming increasingly worrisome. With the widespread use of Android-powered mobile devices, attackers are leveraging zero-day vulnerabilities to infect Android software at an alarming rate. Detecting zero-day vulnerabilities in Android applications is particularly challenging due to their unpatched and undiscovered nature, resulting in a lack of reference points for identification. In response to this issue, we propose a novel and effective system called Zero-Vuln, which is designed to classify and identify zero-day Android malware. Zero-Vuln leverages deep learning and zero-shot learning techniques, as well as established data-sets, to identify previously unknown malware. Our approach achieves a remarkable performance of 83% accuracy, as well as high precision and recall, and represents a significant contribution to the field of cyber security. © 2023 IEEE.

关键词： Zero-day attack

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Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models

Modeling stochastic eye tracking data: A comparison of quant...

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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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Predictive Maintenance with Large Language Models and Transformer-based Survival analysis 50

Predictive Maintenance with Large Language Models and Transf...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Esteban, Aurora Cobilean, Victor Nawaratne, Rashmika Dept. of Computer Science and Numerical Analysis University of Cordoba Cordoba Spain Dept. of Computer Science Virginia Commonwealth University RichmondVA United States Research Center for Data Analytics and Cognition La Trobe University VIC Australia

ISBN: (纸本)9781665464543

Predictive maintenance involves proactive monitoring and prediction of equipment failures to optimize maintenance schedules to minimize downtime, reduce losses, and ensure optimal equipment performance. However, traditional methods often rely on classification or regression, overlooking the temporal aspect of failure prediction. Survival analysis is focused on modeling time-to-event data, making it well-suited for predicting the time until equipment failures. The primary goal of this paper is to evaluate the feasibility and effectiveness of Transformer-based model for survival analysis in the industrial domain. Additionally, this work aims to explore the integration of Large Language Models as virtual assistants to facilitate user interaction and decision-making in the predictive maintenance process. The proposed system was tested on a public predictive maintenance problem and the Transformer-based model obtained better survival predictions than the baseline statistical and machine learning models, with improvements of up to 20%. Furthermore, the virtual assistant based on the large language model demonstrated the ability to retrieve precise information based on natural language prompts. © 2024 IEEE.

关键词： Predictive maintenance

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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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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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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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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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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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