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Uniandes at the Regulations Challenge Task: A Scalable Framework for Legal Text Understanding in Regulatory and Financial Contexts 9

Uniandes at the Regulations Challenge Task: A Scalable Frame...

引用

Joint Workshop of the 9th Financial Technology and Natural Language Processing, FinNLP 2025, the 6th Financial Narrative Processing, FNP 2025, and the 1st Workshop on Large Language Models for Finance and Legal, LLMFinLegal 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Carrión, Santiago Martínez Castañeda, Juan Manuel Manrique, Rubén Francisco Dept. of Systems and Computing Engineering Universidad de los Andes Bogotá Colombia

ISBN: (纸本)9798891762091

This study presents a comprehensive approach to developing a domain-specific large language model (LLM) for regulatory and financial text interpretation. A specialized corpus was constructed through large-scale scraping of financial and regulatory documents across domains such as compliance, licensing, and financial reporting. The data was preprocessed using GPT-4o-mini with prompt engineering to retain critical information and remove noise. We further pre-trained a LLaMA-3.1-8B model on the curated corpus and fine-tuned it using an instruction dataset covering nine tasks from the Coling 2025 Regulations Challenge (Wang et al., 2024), including acronym expansion, regulatory question-answering, and XBRL-based financial analytics, employing QLoRA to reduce memory requirements. The model exhibits a slight improvement from baseline answering complex regulatory questions (detailed QA) and expanding acronyms. This study demonstrates the potential of domain-specific LLMs in regulatory text interpretation and lays the groundwork for future research in specialized NLP evaluation methodologies. © 2025 Association for Computational Linguistics.

关键词： Finance

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Impact of Artificial Intelligence on Decision-Making in Information systems 5

Impact of Artificial Intelligence on Decision-Making in Info...

引用

5th International Conference on Advanced Research in computing, ICARC 2025

作者： Rajasinghe, Rwmr Dharani Faculty of Computing Nsbm Green University Dept. of Software Engineering & Information Systems Homagama Sri Lanka

ISBN: (纸本)9798331530983

The study examines how artificial intelligence (AI) affects information systems (IS) decision-making, emphasizing how AI improves precision, speed, and flexibility in a range of industries. AI presents potential and problems, including ethical, legal, and societal ramifications, as it revolutionizes organizational decision-making. In order to demonstrate AI's function in intricate IS systems, this paper examines a variety of AI strategies used in decision-making, assesses their efficacy using accepted metrics, and examines real-world case studies. Key findings address issues including data bias, lack of transparency, and job displacement while highlighting the advantages of AI in processing massive information and producing useful insights. The recommendations highlight the necessity of worker reskilling, sector-specific adaptability, explainable AI (XAI) models, and regulatory frameworks. In order to facilitate sustainable AI integration in IS for improved organizational and social results, this research emphasizes the significance of striking a balance between AI-driven innovation and ethical issues. © 2025 IEEE.

关键词： Risk perception

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Analysing the Roles of Robotics in Manufacturing Organizations in the Era of Industry 4.0

Analysing the Roles of Robotics in Manufacturing Organizatio...

引用

2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adetunla, Adedotun Akinlabi, Esther Jen, Tien Chien Ajibade, Samuel-Soma University of Johannesburg Dept. of Mechanical Engineering Science South Africa Northumbria University Dept. of Mechanical & Construction Engineering Newcastle United Kingdom Sunway University School of Engineering & Technology Dept. of Computing & Information Systems Malaysia

ISBN: (纸本)9798350358155

The era of Industry 4.0 has ushered in a new approach in manufacturing, leveraging advanced technologies to transform traditional production methods. Robotics is the science of using robots to improve manufacturing processes with automation, flexibility, and efficiency. This article aims to comprehensively examine the role of robotics in manufacturing within the context of Industry 4.0. It seeks to explore the fundamental principles underlying robotics, their integration with Industry 4.0 technologies, applications across diverse manufacturing sectors, challenges faced in implementation, and the future directions of robotics in shaping the manufacturing landscape. © 2024 IEEE.

关键词： Smart manufacturing

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Harnessing the Power of Artificial Intelligence in Materials Science: An Overview

Harnessing the Power of Artificial Intelligence in Materials...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

ISBN: (纸本)9798350358155

The integration of artificial intelligence (AI) into the realm of material science has ushered in a new era, by changing the process of material discovery and design. Leveraging advanced computational methods, machine learning algorithms, and predictive modeling, AI accelerates the identification of novel materials with tailored properties. From quantum simulations to high-throughput experimentation, AI-driven techniques enable rapid screening and prediction of material behaviors, significantly reducing the time and resources traditionally required for innovation. This synergy has paved the way for the creation of smart and adaptive materials, responsive to external stimuli and tailored for specific applications across industries. The marriage of AI and material science extends beyond discovery, encompassing efficient process optimization, manufacturing improvements, and the management of vast datasets through materials informatics. Challenges, including ethical considerations, data privacy, and responsible AI practices, must be navigated for sustainable integration. Looking forward, the collaborative potential of AI and material science promises continuous advancements. Ongoing research in machine learning, deep learning, and materials informatics anticipates breakthroughs in material applications, pushing the boundaries of innovation and sustainability. The union of AI and material science not only reshapes the landscape of scientific discovery but also holds the key to unlocking unprecedented opportunities for intelligent materials that will define the technological future. © 2024 IEEE.

关键词： Artificial Intelligence Machine Learning Materials Characterization Materials Science

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BiBE: A Self-supervised Contrastive Learning Architecture for Malware Detection 11

BiBE: A Self-supervised Contrastive Learning Architecture fo...

引用

11th IEEE International Conference on Computer Science and Network Technology, ICCSNT 2023

作者： Wang, Yu Xu, Mingdi Luo, Kexin Tong, Hui Jin, Chaoyang Xie, Bo Wuhan Digital Engineering Institute Dept. of Computing Environment Wuhan China Wuhan Digital Engineering Institute Dept. of Systems Engineering Wuhan China

ISBN: (纸本)9798350311594

Malware detection has become significantly more difficult due to the constant evolution of malware and the emergence of various variants. While machine learning-based malware detection method solves the limitations of traditional methods to a certain extent, the vast majority of current work uses supervised learning methods, which require large sets of labeled data, which means a huge amount of manpower and time cost. Self-supervised learning can solve these problems by extracting information from unlabeled data for learning. In this paper, we introduce BiBE, a malware detection architecture based on self-supervised learning framework. We first preprocess the samples with normalization, complete the missing information of features, and perform vectorization processing. Then, after learning and encoding the sample features by setting proxy tasks, we use comparative learning to distinguish similar samples, achieve the division of positive and negative samples and assign pseudo-labels. Finally, the labeled samples and pseudo-labeled samples are put into the Bi-LSTM network to train the classifier to detect malware. Moreover, we also evaluate the model on a public dataset. The evaluation shows that the BiBE model has good convergence and generalization in training, achieves excellent detection performance, and outperforms other existing methods, verifying the feasibility of the BiBE model. © 2023 IEEE.

关键词： Supervised learning

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DWA-based Dynamic Obstacle Avoidance Planning Method for USVs 11

DWA-based Dynamic Obstacle Avoidance Planning Method for USV...

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11th IEEE International Conference on Computer Science and Network Technology, ICCSNT 2023

作者： Luo, Kexin Chen, Shiyou Wang, Yu Wuhan Digital Engineering Institute Dept. of Systems Engneering Wuhan China Wuhan Digital Engineering Institute Dept. of Computing Environment Wuhan China

ISBN: (纸本)9798350311594

To solve the problem of environment perceived instability in the process of autonomous navigation of USVs, a local planning method based on DYNAMIC WINDOWS APPROACH is proposed for the known static obstacles and unknown dynamic obstacles collision avoidance planning in the navigation area of USVs, and combined with the Velocity Obstacle method, the velocity obstacle region is incorporated in the dynamic window to further optimize the speed constrained It also sets the fusion to start distance and limit distance in the evaluation function to take into account the safety and economy of the planning and improves the efficiency of the dynamic window method to evaluate the predicted trajectory for finding the best. The simulation results verify that the improved DWA can achieve automatic obstacle avoidance in complex environments, and ensure the safety of obstacle avoidance navigation while the planning path is more reasonable and smooth, avoiding the USVs from bypassing outside the dense obstacle cluster, and the number of iterations and running time are reduced by more than 15%. The research results have more obvious scientific value and practical significance to the development of autonomous obstacle avoidance technology for unmanned vehicles. © 2023 IEEE.

关键词： Collision avoidance

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Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study 59

Synthetic Time Series Data Generation for Healthcare Applica...

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59th Annual Conference on Information Sciences and systems, CISS 2025

作者： Jamshidi, Ainaz Arif, Muhammad Kalhoro, Sabir Ali Gelbukh, Alexander University of Maryland Baltimore County Dept. of Information Systems United States Colorado State University Dept. of Information Systems United States North Dakota State University Dept. of Electrical Engineering United States Instituto Politécnico Nacional Dept. of Computing Research Center Mexico

ISBN: (纸本)9798331513269

The generation of high-quality medical time series data is essential for advancing healthcare diagnostics and safeguarding patient privacy. Specifically, synthesizing realistic phonocardiogram (PCG) signals offers significant potential as a cost-effective and efficient tool for cardiac disease pre-screening. Despite its potential, the synthesis of PCG signals for this specific application received limited attention in research. In this study, we employ and compare three state-of-the-art generative models from different categories - WaveNet, DoppelGANger, and DiffWave - to generate high-quality PCG data. We use data from the George B. Moody PhysioNet Challenge 2022. Our methods are evaluated using various metrics widely used in the previous literature in the domain of time series data generation, such as mean absolute error and maximum mean discrepancy. Our results demonstrate that the generated PCG data closely resembles the original datasets, indicating the effectiveness of our generative models in producing realistic synthetic PCG data. In our future work, we plan to incorporate this method into a data augmentation pipeline to synthesize abnormal PCG signals with heart murmurs, in order to address the current scarcity of abnormal data. We hope to improve the robustness and accuracy of diagnostic tools in cardiology, enhancing their effectiveness in detecting heart murmurs. © 2025 IEEE.

关键词： Cardiology

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Energy Efficient joint user association and power allocation using Parameterized Deep DQN 9

Energy Efficient joint user association and power allocation...

引用

9th International Conference on Computer and Communication engineering, ICCCE 2023

作者： Mughees, Amna Tahir, Mohammad Sheikh, Muhammad Aman Amphawan, Angela Yap, Kian Meng Habaebi, Mohamed Hadi Islam, Md. Rafiqul Sunway University Dept. of Computing and Information Systems Petaling Jaya Malaysia University of Turku Dept. of Computing Turku Finland Cardiff Metropolitan University Dept. of Electronics and Computer Systems Engineering Cardiff United Kingdom International Islamic University Dept. of Electrical and Computer Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350325218

Using small cells to create an ultra-dense network for 5G and beyond is a promising strategy to improve network coverage, data demands and reduce latency. Despite using small cells, these dense wireless networks result in performance degradation and increased energy consumption. Energy consumption is a crucial parameter for sustainable future wireless networks. In order to improve quality of service (QoS) and Energy Efficiency (EE), efficient resource allocation strategies are required. This paper investigates a Parameterized Double Deep Q-Network (PDDQN) based framework for joint user association and power allocation to improve EE and throughput. Apart from other conventional machine learning approaches, considering single state space of the joint optimization problem, our proposed framework considers both discrete and continuous state spaces. Our proposed PDDQN technique also solves the generalization problem that occurs due to similar states. The simulation results indicate that the proposed work significantly improves energy EE and throughput in large-scale learning problems. © 2023 IEEE.

关键词： Energy efficiency

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Performance Evaluation of an Electronics Walking Stick for the Blind

Performance Evaluation of an Electronics Walking Stick for t...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adetunla, Adedotun Akinlabi, Esther Jen, Tien Chien Oke, Wasiu Ajibade, Samuel-Soma University of Johannesburg Dept. of Mechanical Engineering Science South Africa Northumbria University Dept. of Mechanical & Construction Engineering Newcastle United Kingdom Afe Babalola University Dept. of Mechanical & Mechatronics Engineering Ado Nigeria Sunway University School of Engineering & Technology Dept. of Computing & Information Systems Malaysia

ISBN: (纸本)9798350358155

Millions of visually impaired or blind individuals face difficulties when navigating independently. While the white cane has been a traditional tool for blind navigation, recent efforts have sought to enhance its capabilities with the integration of sensors. Although blind individuals possess keen haptic sensitivity, challenges arise when using the white cane outdoors. Therefore, this study aims to improve the independence of blind individuals during both indoor and outdoor navigation. The key components employed in this research include ultrasonic sensors, Arduino Nano, Global System for Mobile Communication (GSM) Module, Global Positioning System (GPS) Module, among others. Testing of the developed electronic walking stick revealed that it not only facilitates a more convenient lifestyle for the blind or visually impaired but also serves as an alert system, notifying them of potential dangers.. © 2024 IEEE.

关键词： Global positioning system

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A review on the Challenges and Prospects in Harnessing Virtual Reality in Education

A review on the Challenges and Prospects in Harnessing Virtu...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adetunla, Adedotun Akinlabi, Esther Jen, Tien Chien Okokpujie, Imhade P. Ajibade, Samuel-Soma University of Johannesburg Dept. of Mechanical Engineering Science South Africa Northumbria University Dept. of Mechanical & Construction Engineering Newcastle United Kingdom Afe Babalola University Dept. of Mechanical & Mechatronics Engineering Ado Nigeria Sunway University School of Engineering & Technology Dept. of Computing & Information Systems Malaysia

ISBN: (纸本)9798350358155

Virtual Reality (VR) has emerged as a revolutionary technology with immense potential in transforming the landscape of education. This manuscript explores the integration of VR into educational settings, examining its benefits, challenges, and the promising future it holds in enhancing teaching and learning experiences across various disciplines. The result of this study shows that the existing literature showcases the diverse applications of VR, ranging from immersive simulations in medical training to enhancing language acquisition and making science, technology, engineering and mathematics (STEM) subjects more accessible. As educators and researchers continue to explore the possibilities offered by virtual reality, it is evident that this technology has the potential to redefine traditional educational paradigms. The emphasis on creating inclusive, interactive, and dynamic learning environments through VR not only caters to diverse learning styles but also addresses the ever-evolving needs of modern education. As this field evolves, ongoing research endeavors will undoubtedly uncover new insights, refine existing applications, and contribute to the continual improvement of educational methodologies in the digital age. © 2024 IEEE.

关键词： Virtual environments

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