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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Chen, Yaqi Zhang, Hao Yang, Xukui Zhang, Wenlin Qu, Dan School of Information Systems Engineering Information Engineering University China Laboratory for Advanced Computing and Intelligence Engineering China

ISBN: (纸本)9782493814104

Self-supervised models have demonstrated remarkable performance in speech processing by learning latent representations from large amounts of unlabeled data. Although these models yield promising results on low-resource languages, the computational expense of fine-tuning all model parameters is prohibitively high. Adapters offer a solution by incorporating lightweight bottleneck structures into pre-trained models, enabling efficient parameter adaptation for downstream tasks. However, randomly initialized adapters often underperform in low-resource scenarios, limiting their applicability in low-resource languages. To address this issue, we develop the Meta-Adapter for self-supervised models to obtain meta-initialized parameters that facilitate quick adaptation to low-resource languages. Extensive experiments on the Common Voice and FLEURS datasets demonstrate the superior performance of Meta-Adapters on 12 low-resource languages spanning four different language families. Moreover, Meta-adapters show better generalization and extensibility than traditional pretraining methods. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Speech processing

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Traffic Sign Detection Using YOLOv8 2

Traffic Sign Detection Using YOLOv8

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Kumar, Rahul Gupta, Aniket Rajeswari, D. School of Computing College of Engineering and Technology Department of Data Science and Business Systems India

ISBN: (纸本)9798350327533

Traffic signals and other signs like parking., stop signs., etc. have become very crucial in autonomous and s elf-driving cars as it helps the smart system to comply with the basic traffic rules along with that it helps navigate routes based on the signs thus enabling a more secure driving experience for the drivers. There have been a lot of new algorithms that have emerged in the past recent years regarding this. In this paper., this research has used the new YOLOv8 object detection system to help us detect traffic signs as it is much fas ter and more precis e than its previous iterations. To improve the algorithm., this paper has used a dataset comprising photos of traffic signs taken at different angles and different light intensities. This system can predict the traffic signs with 93% accuracy. © 2024 IEEE.

关键词： Autonomous vehicle Deep Learning Traffic sign detection yolov8

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Framework for Enhancing Customer Service Advisor Training through Gamification 14

Framework for Enhancing Customer Service Advisor Training th...

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14th International Conference on Cloud computing, Data Science and engineering, Confluence 2024

作者： Sierra, Christian Peña Cutipa, Enrique Agreda Wong, Lenis School of Systems Engineering and Computing Universidad Peruana de Ciencias Aplicadas Lima Peru

ISBN: (纸本)9798350344837

In the Peruvian financial sector, deficiencies persist in customer service training due to inadequate comprehension of work motivation and the absence of suitable incentives, reflective of concerns regarding skill shortages and operational inconsistencies among employees. This study proposes a gamification framework tailored to enhance the training of Customer Service advisors within financial entities, addressing the gap in specific training models for this crucial sector. Through analysis, design, and validation stages, the research scrutinizes existing methodologies and implements the framework within a financial Contact Center, yielding impressive results. Notably, a remarkable 85% reduction in training time (from 60 to 8.80 minutes) while maintaining consistent evaluation scores with a 1% increase, alongside a noteworthy 13% decrease in average evaluation duration, underscores the framework's efficacy. These findings emphasize its potential for continual enhancement in training practices, promoting heightened productivity, knowledge retention, and motivation among Customer Service advisors within the financial landscape. © 2024 IEEE.

关键词： Personnel training

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Parameter-efficient fine-tuning of Whisper for low-resource speech recognition 5

Parameter-efficient fine-tuning of Whisper for low-resource ...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Liu, Yunpeng Qu, Dan School of Information Systems Engineering University of Information Engineering Laboratory For Advanced Computing and Intelligence Engineering Department of Artificial Intelligence Henan China Systems Engineering University of Information Engineering Laboratory For Advanced Computing and Intelligence Engineering Henan China

ISBN: (纸本)9798350385557

Limited data availability remains a significant challenge for Whisper's low-resource speech recognition performance, falling short of practical application requirements. While previous studies have successfully reduced the recognition error rates of target language speech through fine-tuning, a comprehensive exploration and analysis of Whisper's parameter-efficient fine-tuning capabilities, the advantages and disadvantages are still lacking. This paper aims to fill this gap by conducting comprehensive experimental exploration for Whisper's low-resource speech recognition performance using two parameter-efficient fine-tuning strategies with limited supervised data from multiple low-resource languages. The results and analysis demonstrate that all fine-tuning strategies explored in this paper significantly enhance Whisper's performance. However, different strategies vary in their suitability and practical effectiveness, highlighting the need for careful selection based on specific use cases and resources available. © 2024 IEEE.

关键词： Speech recognition

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AFuNet: an attention-based fusion network to classify texts in a resource-constrained language

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Neural computing and Applications 2025年第9期37卷 6725-6748页

作者： Hossain, Md. Rajib Hoque, Mohammed Moshiul Dewan, M. Ali Akber Hoque, Enamul Siddique, Nazmul Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada School of Information Technology York University TorontoONM3J 1P3 Canada School of Computing Engineering and Intelligent Systems Ulster University Coleraine United Kingdom

In the era of widespread Internet use and extensive social media interaction, the digital realm is accumulating vast amounts of unstructured text data. This unstructured data often contain undesirable information, necessitating time-consuming manual classification efforts. An intelligent text classification system capable of automatically categorizing digitized texts based on semantic meaning is crucial. However, this task is particularly challenging for low-resource languages like Bengali due to a shortage of annotated corpora, issues with out-of-vocabulary words, lack of domain-specific hyperparameter tuning, limited ability to extract generalized text features, and class imbalances within the corpus. AFuNet: an attention-based fusion network to classify texts in a resource-constrained language. AFuNet undergoes a comprehensive four-phase experimental process, including baseline model evaluation and hyperparameter tuning, late fusion and model selection, attention-based early fusion and model identification, and an ablation study with impact analysis. Fine-tuned based on five Bengali text classification corpora, AFuNet achieves impressive accuracies: 96.60 ± 0.2 (BTCC11), 85.37 ± 0.2 (OSBC), 97.35 ± 0.2 (BARD), 93.74 ± 0.2 (IndicNLP), and 96.51 ± 0.2 (ProthomAlo). In comparison with previous state-of-the-art models on these corpora, AFuNet demonstrates significant accuracy improvements ranging from 0.54% to 4.49%, showcasing its effectiveness in advancing text classification capabilities for the Bengali language. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based Computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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Simulating a Multi-Agent UAV System Coordinated by State Machines Using Godot 10

Simulating a Multi-Agent UAV System Coordinated by State Mac...

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10th IEEE Smart World Congress, SWC 2024

作者： Howard, Leo Lin, Fuhua Leung, Henry Athabasca University School of Computing and Information Systems Canada University of Calgary Schulich School of Engineering Calgary Canada

ISBN: (纸本)9798331520861

Unmanned aerial vehicles have advanced quickly and are now a dominant force in several domains, including military, security, and even logistics. 3D simulations with virtual environments are cost effective tools for assessing feasibility and costs of developing new systems or upgrading current systems. This is especially true for multiagent or swarm systems that require many agents working in concert. The total costs can be much higher when only physical prototypes are used for testing and analysis. There are existing 3D libraries for aiding with UAV simulations, but they're too specialized;it is difficult to create highly customized virtual environments with these packages. A simulation development framework needs to be capable and stable for accurate simulations yet generalized enough that any types of simulations can be developed without exhausting efforts. This paper explores the feasibility and ease of using Godot, an open-source game engine, to develop a simulation that can assess the function of a multiagent UAV system. It is found that the Godot engine is a powerful tool that is both accurate and generalized enough to develop highly customizable UAV simulation. This allows verification and testing of UAV designs and control algorithms in highly customizable and flexible virtual environments, using a free open-sourced tool. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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INTEGRATING UNMANNED AERIAL systems INTO THE INTELLIGENT systems engineering CURRICULUM 34th

INTEGRATING UNMANNED AERIAL SYSTEMS INTO THE INTELLIGENT SYS...

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34th Congress of the International Council of the Aeronautical Sciences, ICAS 2024

作者： Dantsker, Or D. Department of Intelligent Systems Engineering Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

This paper presents two hands-on, project-based courses on unmanned aerial systems recently offered by the Intelligent systems engineering program at Indiana University. In Fall 2023, ENGR-E399/599 Autonomous Sports was redeveloped to focus on integrated vehicle design and development of lighter-than-air vehicles in a capstone-like, project-based course format. And in Spring 2024, ENGR-E399/599 Unmanned Aerial systems was developed to introduce students to UAS in a lecture-lab-project format where students learned about underlying aircraft concepts, developed a micro-controller-based autopilot, ultimately integrated it into a small fixed-wing unmanned aircraft that was test flown. These courses, geared towards advanced undergraduate and graduate students, allow them to apply their prerequisite knowledge to these hardware-lab heavy courses, enabling experiential learning, i.e. "learn-by-doing". © 2024, International Council of the Aeronautical Sciences. All rights reserved.

关键词： Students

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A Combined Distance Metric Approach with Weight Adjustment For Improving Mixed Data Clustering Quality 6

A Combined Distance Metric Approach with Weight Adjustment F...

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6th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Pyae, Aung Low, Yeh-Ching Chua, Hui Na School of Engineering and Technology Sunway University Department of Computing and Information Systems Subang Jaya Malaysia

ISBN: (纸本)9798350389692

Cluster analysis can be perceived as a problem of grouping data points according to their mutual similarity. Clustering quality largely depends on choosing an effective distance metric, especially when dealing with mixed data that contains both numerical and categorical features. This study introduces a Weighted Combined distance metric using Gower and sqrt-cosine distances to improve clustering quality for mixed data. By combining two individual distance metrics with weights, this unique technique generates an overall similarity score that can improve the clustering quality. Experiments have demonstrated that the Weighted Combined distance metric can yield superior clustering quality compared to individual distance metrics. These findings highlight the potential of this proposed method to effectively cluster mixed data and overcome challenges associated with mixed data clustering. © 2024 IEEE.

关键词： Distance Metrics Hierarchical Clustering Mixed Data Unsupervised Learning

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Enhancing Security: Evaluating RFID Card Access Control systems using SHA-256

Enhancing Security: Evaluating RFID Card Access Control Syst...

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2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Yang, You Zhi Ramly, Athirah Mohd Sharif, Mhd Saeed Department of Computing and Information Systems Sunway University Selangor Malaysia School of Architecture Computing and Engineering UEL London United Kingdom Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom

ISBN: (纸本)9798331533137

This paper focuses on enhancing the security of a chosen system by implementing multiple layers of protection, including concealing sensitive data on the Liquid Crystal Display (LCD), utilizing hashed Unique Identifiers (UIDs), and deploying biometric dual-factor authentication via fingerprint recognition. The system is considered secure as it prevents unauthorized access attempts, even if the tag appears legitimate. The paper covers various stages, from component procurement to vulnerability assessment, exploitation, and solution development. While this paper significantly improves system security, further investigation remains crucial. Therefore, this paper not only addresses immediate security implementations but also paves the way for ongoing analysis and improvements aimed at defending the system against malicious threats while optimizing its performance. ©2024 IEEE.

关键词： Liquid crystal displays

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