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1st Workshop on Language Models for Low-Resource Languages, LoResLM 2025 - co-located at the 31st International Conference on Computational Linguistics, COLING 2025

作者： Lasheras, Uriel Pinheiro, Vládia Postgraduate Program in Applied Informatics University of Fortaleza Ceará Brazil

ISBN: (纸本)9798891762152

Large Language Models (LLMs) are increasingly central to the development of generative AI across diverse fields. While some anticipate these models may mark a step toward artificial general intelligence, their ability to handle complex causal reasoning remains unproven. Causal reasoning is essential for true general intelligence, particularly at Pearl’s interventional and counterfactual levels. In this work, we introduce ***, a dataset of over 8,000 natural causal questions in Portuguese, collected from real human interactions. Built upon a novel three-axis taxonomy, *** categorizes questions by causal intent, action requirements, and the level of causal reasoning needed (associational, interventional, or counterfactual). Our findings from evaluating ***’s seed questions with GPT-4o reveal that this LLM faces challenges in handling interventional and relation-seeking causal queries. These results suggest limitations in using GPT-4o for extending causal question annotations and highlight the need for improved LLM strategies in causal reasoning. *** provides a foundation for advancing LLM capabilities in causal understanding, particularly for the Portuguese-speaking world. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Analysis of the Recognition of Facial Movements of App Designed for Rehabilitation of People with Facial Paralysis

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第13期18卷 112-129页

作者： Corrêa, Ana Grasielle Dionísio Ravaglio, Anna de Souza Cruz Navikas, Flávio Hergersheimer Do Amaral, Jucelio Delmondes Filho, Paulo César Machado Cunha, Daniela Vieira Silva, Matheus Gois de Lima Rodrigues, Bruno da Silva Faculty of Computing and Informatics and the Postgraduate Program in Applied Computing Mackenzie Presbiterian University São Paulo Brazil

Peripheral facial paralysis (PFP) causes deficits in muscle and sensory functions of the face due to damage to the facial nerve. In this study, we evaluated the effectiveness of the "Fisiobem" app in rehabilitating patients with PFP through facial mimicry exercises. The app uses the device’s camera to identify the user and measure the range of motion during the exercises, applying the ARKit framework to quantify the intensity of the movements. We conducted a usability study with 34 participants unaffected by PFP, performing mimics with different amplitudes. Results show that glasses did not affect recognition accuracy in exercises such as raising eyebrows, frowning, and closing the eyes tightly. As for wrinkling the nose exercise, glasses impacted higher intensities. As for the beard or mustache, lower lip protrusion, compressing the lips, smiling, showing teeth and blowing out filling cheeks were not affected. However, when smiling with lips together, beard and mustache influenced intensities above 75%. It is important to highlight that this study has limitations, such as the sample size and lack of consideration for ethnic diversity among participants. Future research with larger and more diverse samples is needed to validate and generalize these findings and explore the nuances of interactions between facial features and facial recognition algorithms. Despite these limitations, the approach facilitated by the "Fisiobem" application offers significant benefits, especially for patients with PFP who face challenges in accessing specialized therapists. It allows patients to complement their treatment by performing guided facial mimicry exercises at home. The application helps individuals accurately perform the exercises and effectively track the progress of their rehabilitation. © 2024 by the authors of this article.

关键词： Glass

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A Methodology for Risk Management of Generative AI based Systems 32

A Methodology for Risk Management of Generative AI based Sys...

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32nd International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2024

作者： Filho, Raimir Holanda Colares, Daniel University of Fortaleza Postgraduate Program in Applied Informatics Fortaleza Brazil

ISBN: (纸本)9789532901382

This paper addresses cybersecurity challenges in integrating Generative Artificial Intelligence (GenAI) into applications. It proposes a risk analysis approach that focuses on identifying threats and vulnerabilities exploited by malicious actors, which is validated through a case study on prompt injection attacks. The results demonstrated the efficacy of our approach in identifying and quantifying vulnerabilities in Large Language Models (LLMs). © 2024 University of Split, FESB.

关键词： Cyber Security Generative AI Large Language Models Risk Management

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Information Security Challenges in the Digitalisation of the Austrian Food Industry: Assessment of Food Suppliers and Implications 29

Information Security Challenges in the Digitalisation of the...

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29th International Conference on Engineering, Technology, and Innovation, ICE 2023

作者： Hetzenauer, Claudia Dobler, Martin Simma, Armin Vorarlberg University of Applied Sciences Master Program Business Administration: Bpm Dornbirn Austria Vorarlberg University of Applied Sciences Research Centre Business Informatics Dornbirn Austria Vorarlberg University of Applied Sciences Competence Area Informatics Dornbirn Austria

ISBN: (纸本)9798350315172

In 2021, a prominent Austria dairy producer suffered from an IT attack and was completely paralysed. Without clearly defined mitigation measures in place, major disruptions were caused alongside the whole supply chain, including logistics service providers, governmental food safety bodies, as well as retailers (i.e., supermarkets and convenience stores). In this paper, we ask the question how digitisation and digital transformation impact IT security, especially when considering the complex company ecosystems of food production and food supply chains in Austria. The problem statement stems from a gap in knowledge of key differences in approaches towards IT security, resilience, risk management and especially business interfaces between food suppliers, supermarkets, distributors, logistics and other service providers. In order to answer related research questions, firstly, the authors conduct literature research, and highlight common guidelines and standardisation as well as look at state-based recommendations for critical infrastructure. In a second step, the paper describes a quantitative and qualitative survey with Austrian food companies (producers and retailers) which is described in detail in the paper. A description of recommended measures for the industry, further steps, as well as an outlook conclude the paper. (Abstract) © 2023 IEEE.

关键词： Supply chain management

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A Methodology for Risk Management of Generative AI based Systems

A Methodology for Risk Management of Generative AI based Sys...

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International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

作者： Raimir Holanda Filho Daniel Colares Postgraduate Program in Applied Informatics University of Fortaleza Fortaleza Brazil

ISBN: (数字)9789532901382

ISBN: (纸本)9798350354614

关键词： Generative AI Computational modeling Large language models Software Computer networks Telecommunications Risk management Computer crime Best practices

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Hybrid model for early identification post-Covid-19 sequelae

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 14739-14752页

作者： de Andrade, Evandro Carvalho Pinheiro, Luana Ibiapina C. C. Pinheiro, Plácido Rogério Nunes, Luciano Comin Pinheiro, Mirian Calíope Dantas Pereira, Maria Lúcia Duarte de Abreu, Wilson Correia Filho, Raimir Holanda Simão Filho, Marum Pinheiro, Pedro Gabriel C. D. Nunes, Rafael Espíndola Comin Graduate Program in Applied Informatics University of Fortaleza Ceará Fortaleza Brazil Ceara State University Ceara Fortaleza Brazil University Center September 7 Ceara Fortaleza Brazil School of Nursing of Porto Porto Portugal

Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Decision support systems

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Multiplicative Seasonal ARIMA Modeling and Forecasting of COVID_19 Daily Deaths in Hungary 10

Multiplicative Seasonal ARIMA Modeling and Forecasting of CO...

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10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022

作者： Chudo, Solomon Buke Program of Applied Information Technology and Its Theoretical Background Doctoral School of Informatics University of Debrecen Debrecen Hungary

ISBN: (纸本)9781665401081

The coronavirus disease (COVID-19) is a terrifying pandemic that is rapidly spreading over the world. Up to this point, Hungary has had a significant COVID-19 death rate. The main purpose of this article is to model and forecast basic seasonal time series for COVID-19 death rates. The COVID 19 data, which was collected between 2020-10-04 and 2021-05-12 by the Hungarian government and the World Health Organization (WHO), has been used. The data was analyzed and models were fitted using R software version 4.1.2. The statistical time series model is fitted with the Multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Forecasts are made using the fitted model. The data output is used to find seasonality, trend patterns, and unstable variance patterns in the time series plot. The trend is made stationary using the starting difference of the converted data approach, and the variance is made constant using the logarithmic transformation of the original data set. Based on the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plot data, the ARIMA (1, 1, 2) (1, 0, 1) (7) model is proposed. The standardized residuals, ACF of residuals, normal Q-Q plot, and p-value for Ljung-Box statistics of the fitted model were found to be within confidence limits and to have no distinct behavioral pattern. The ARIMA (1, 1, 2) (1, 0, 1) (7) model has the smallest estimated value, with a sigma square estimated value of 0.02764, log-likelihood = 80.41, and an Akaike Information Criterion (AIC) value of 148.82. As a consequence, the fitted model ARIMA (1,1,2) (1,0,1) (7) is identified as the best model for forecasting the COVID-19 daily death rate in the country. © 2022 IEEE.

关键词： COVID-19

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Software Quality Practices in Growing Startups: A Qualitative Study 20

Software Quality Practices in Growing Startups: A Qualitativ...

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20th Brazilian Symposium on Software Quality, SBQS 2021

作者： Pizzini, Adriano Bortolo Vieira, Ricardo Deda Gomes, Rhodrigo Santos, Gleison Malucelli, Andreia Reinehr, Sheila Graduate Program in Informatics Pontifical Catholic University of Paraná Paran Curitiba Brazil Department of Applied Informatics Federal University of the State of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9781450395533

Startups arise in environments of extreme uncertainty, with few resources and the need to scale quickly. In the growth phase, they still need agility, but they tend to concern more with software quality and the development process by having more human and financial resources. Studying the factors that lead startups in the growth phase to seek quality in their processes can help new startups anticipate implementing quality practices. In this paper, we aim to identify how startups adopt quality practices in the growth phase. We carried out a multiple case study with four growth phase startups. We applied qualitative analysis techniques to summarize the findings. The studied startups adopt limited quality practices and are influenced by the development team's maturity level, organizational culture, and experience. Furthermore, they tend to be reactive towards quality, solving the problems when they impact their product, business, customer, or when technical debt can no longer be managed. The findings are relevant to support stabilizing and growing startups to define actions to improve the quality of their processes and products. Besides, they suggest that growth phase startups begin a process of change in the organizational culture concerning quality, adopting the behavior of mature companies as soon as they get more human and financial resources. © 2021 ACM.

关键词： Quality control

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Bibliometric Analysis: Research Trends in Leaf Image Segmentation and Classification

Bibliometric Analysis: Research Trends in Leaf Image Segment...

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2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Agustini, Eka Puji Gernowo, Rahmad Kurnianingsih Wibowo, Adi Warsito, Budi Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Doctoral Program of Information System Diponegoro University Semarang Indonesia Master of Applied Science Program Polytechnic Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia Faculty of Science and Mathematics Diponegoro Universitty Department of Statistics Semarang Indonesia

ISBN: (纸本)9798350350524

Exploring various phenomena and issues related to leaf images is paramount, particularly in segmentation and classification of such images. This study employs bibliometric analysis to delve into two overarching themes: The trends in publication and the evolution of publications, along with a keyword-based analysis. The research methodology unfolds in two stages: (1) data collection and (2) analysis. The dataset comprises 1,248 articles sourced from the Scopus database, covering the period from 1988 to 2023. The research findings unveil a noteworthy surge in publication trends, peaking at 231 documents in 2023. An in-depth examination of journal names demonstrates that studies on the segmentation and classification of leaf images are predominantly featured in computer science journals, exemplified by the publication of 589 documents. Furthermore, an analysis of frequently used keywords highlights 'Extraction' as the predominant term, employed a total of 364 times. This underscores that the research focus on leaf image segmentation and classification presents ample opportunities for researchers to delve more profoundly into the subject. © 2024 IEEE.

关键词： Image segmentation

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Seafloor Imaging Maps by Incorporating Side Scan Sonar Technology on Hybrid Autonomous Underwater Glider 6

Seafloor Imaging Maps by Incorporating Side Scan Sonar Techn...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Atmaja, Ardian Budi Kusuma Hidayat, Egi Muhammad Idris Riyanto Trilaksono, Bambang Siregar, Simon Habibullah, Natsir School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Institut Teknologi Bandung Telkom University Doctoral Program of Electrical Engineering Department Computer Engineering Faculty of Applied Science Indonesia

ISBN: (纸本)9798350358346

This research aims to construct a two-dimensional image to represent an underwater geometry map with a Side Scan Sonar (SSS) mounted on a Hybrid Autonomous Underwater Glider (HAUG). Building the underwater map has two stages of the process. The first stage is preprocessing which includes Time-varying gains (TVG), slant range correction, and ground range correction. The second is HAUG navigation, the movement of HAUG orientation to the angle and distance of SSS readings and plotting the geometry of the grid map. With these two stages, a two-dimensional grid map will be formed. Meanwhile, the 3D plot utilizes slant range and ground range correction to obtain the surface height of the bottom of the pool. From the experiments conducted to conduct mapping in the pool, two-dimensional and three-dimensional visual forms of the pool floor and pool walls were produced. The results of the estimated measurement of the width of the pool obtained 14.05 meters and the height of the pool 3.199 meters with an SSS beam angle of 58 degrees. © 2023 IEEE.

关键词： Geometry

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