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GovBERT-BR: A BERT-Based Language Model for Brazilian Portuguese Governmental Data 34th

GovBERT-BR: A BERT-Based Language Model for Brazilian Portug...

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34th Brazilian Conference on Intelligent Systems

作者： Silva, Mariana O. Oliveira, Gabriel P. Costa, Lucas G. L. Pappa, Gisele L. Univ Fed Minas Gerais UFMG Belo Horizonte MG Brazil

ISBN: (纸本)9783031790317;9783031790324

Given the growing interest in natural language processing (NLP) for governmental applications, particularly in Brazil, where vast amounts of governmental data are processed daily, the need for specialized NLP models tailored to the nuances of Brazilian Portuguese and the legal and administrative domains has become increasingly apparent. However, existing models may struggle to accurately interpret the complexities of governmental texts, often leading to suboptimal performance in document classification and analysis tasks. To address these challenges, we introduce GovBERT-BR, a pre-trained language model tailored to the Brazilian governmental context, covering legal and administrative domains. Leveraging insights from diverse governmental texts, GovBERT-BR addresses the challenges of accurately interpreting Brazilian Portuguese and the unique legal and bureaucratic terminology prevalent in governmental documents. We present the pre-training process and experimental evaluation of GovBERT-BR, comparing its performance against baseline models across various text classification tasks relevant to the Brazilian public sector. Our findings demonstrate that GovBERT-BR outperforms existing models in document and short-text classification tasks, showcasing its efficacy in accurately analyzing governmental text data. Furthermore, our analysis reveals insights into the convergence behavior of GovBERT-BR during fine-tuning, highlighting its rapid adaptation to downstream tasks.

关键词： Natural Language Processing Brazilian Governmental Data Legal Texts BERT Language Models

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HVUE Planner: 3D UAV Path Planner Using Hierarchical Visibility Graph in Unknown Environments 17th

HVUE Planner: 3D UAV Path Planner Using Hierarchical Visibil...

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17th International Conference on Intelligent Robotics and Applications

作者： Pang, Jiaxue Chen, Liming Jia, Yijie Chen, Weihuang Zhang, Xuchong Sun, Hongbin Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China 208 Res Inst China Ordnance Ind Beijing 102202 Peoples R China

ISBN: (纸本)9789819607853;9789819607860

In recent years, research on UAV path planning in known environments has matured. However, the demand for planning techniques in unknown environments is increasing in practical engineering applications, where numerous planning needs arise in unknown environments. In such scenarios, the related technologies remain challenging. This paper introduces an efficient 3D UAV Path Planner for Unknown Environments that utilizes a single-layer visibility graph to efficiently handle not only unknown but also partially known environments. It supplements local obstacle handling by using 2D projection maps, aiming to address the high map update storage and computational complexity issues in traditional methods. Extensive experimental results have been tested in a moderately sized virtual campus simulation environment and show that our method is highly efficient with less computational load and map storage compared to state-of-the-art planning methods.

关键词： Path Planning Aerial Systems Unknow Enviroment-based Planning Visibility Graph

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Person Re-identification with Multi-stage Channel Feature Aggregation 17th

Person Re-identification with Multi-stage Channel Feature Ag...

引用

17th International Conference on Intelligent Robotics and Applications

作者： Guo, Hubo Li, Xin Wang, Qiang Zhang, Meiling Huang, Zhihong Shenyang Univ Coll Informat Engn Shenyang 110044 Peoples R China Shenyang Univ Key Lab Mfg Ind Integrated Automat Shenyang 110044 Peoples R China

ISBN: (纸本)9789819607730;9789819607747

In the field of Person re-identification, text-guided semantic information mining has shown strong potential in recent years. The visual language model CLIP, with its excellent cross-modal comprehension capability, has achieved remarkable results in several tasks. In particular, in person re-identification, a two-stage CLIP-based approach has recently been proposed, which attempts to generate corresponding textual descriptions from the labels of specific pedestrians, which in turn aids the pedestrian classification task. However, after in-depth research, it was found that this approach has limitations in the first stage of feature extraction, i.e., the generated features are not sufficient to fully fit the pedestrian's characteristics, which leads to the fact that the textual cues generated by utilizing these features in the second stage are not able to accurately represent the information for pedestrian in the vision. To solve this problem, this paper proposes an innovative Multi-stage Channel feature Aggregation method. The method digs deeper into the feature information of different stages through a well-designed feature aggregation strategy. This design allows our method to learn feature representations that are more attuned to pedestrian characteristics, which in turn generates more accurate textual cues. On the MSMT17, Market1501, DukeMTMC, and Occluded-Duke datasets, we achieve accuracy improvements of 2.4%, 1.0%, 1.5%, and 3.6%, respectively. These results fully validate validity and applicability of our proposed method.

关键词： Person re-identification Multi-stage channel feature aggregation CLIP

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Humanities and AI: Ethical Education in Technology Careers 34th

Humanities and AI: Ethical Education in Technology Careers

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34th Brazilian Conference on Intelligent Systems

作者： Camargo, Laura Simoes Blay, Enio Alterman Schmidt, Gabriela Soares Jose Luiz Egydio Setubal Fdn Av Angelica 2071 BR-01227200 Sao Paulo Brazil Univ Sao Paulo R Reitoria 374 BR-05508220 Sao Paulo Brazil

ISBN: (纸本)9783031790317;9783031790324

This paper presents a quantitative and qualitative study of the teaching of ethics and related subjects in undergraduate courses in Computing of 17 top-ranked universities evaluated in three different ranking systems. The analysis was performed using documents from the selected universities and semi-structured interviews with professors and a head of department. This article discusses the relevance of ethical and social-oriented teaching and classifies the syllabuses of the sampled disciplines, assessing the type of focus that is apparently given. The overall results show considerable variability in the availability of data, ranging from courses whose content is very detailed to those that don't present their syllabus at all. Considering that the development of critical reasoning based on ethics is extremely relevant to the education of future artificial intelligence developers, the authors concluded that universities are not fulfilling their role adequately.

关键词： artificial intelligence Ethics Human Rights Computing Education

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Semi-supervised Predictive Clustering Trees for Multi-label Protein Subcellular Localization 34th

Semi-supervised Predictive Clustering Trees for Multi-label ...

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34th Brazilian Conference on Intelligent Systems

作者： Alcantara, Leonardo U. Triguero, Isaac Cerri, Ricardo Univ Fed Sao Carlos Dept Comp Sao Carlos SP Brazil Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain Univ Sao Paulo Inst Math & Comp Sci Sao Carlos SP Brazil

ISBN: (纸本)9783031790317;9783031790324

Protein subcellular localization is an important classification task because the location of proteins in a cell is directly linked to their functions. Since a protein can act at two or more locations simultaneously, multi-label classification algorithms are necessary. The currently used algorithms are usually based on supervised learning, which presents some disadvantages such as (i) a need for a large amount of labeled instances for training;(ii) a waste of valuable information that labeled instances can provide;and (iii) a high cost involved in obtaining labeled instances for training. To overcome these disadvantages, semi-supervised learning can be applied, where classifiers exploit both labeled and unlabeled data. Thus, in this paper, we propose a new semi-supervised algorithm for multi-label protein subcellular localization. Our proposal is based on decision tree classifiers induced using predictive clustering trees. We investigate many semi-supervised protein subcellular localization scenarios to test whether unlabeled instances can improve the multi-label classification process. Our results show that the proposal can achieve competitive or better results when compared to the pure supervised version of the predictive clustering trees.

关键词： predictive clustering trees protein subcellular localization multi-label classification semi-supervised learning

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RESTful API for Intent Recognition Based on RASA 23rd

RESTful API for Intent Recognition Based on RASA

引用

23rd Mexican International Conference on artificial intelligence

作者： Garofalo-Jerez, Vicente Samuel Hojas-Mazo, Wenny Moreno-Espino, Mailyn Villuendas-Rey, Yenny Lopez-Gonzalez, Ariel Macia-Perez, Francisco Berna-Martinez, Jose Vicente Univ Tecnol La Habana Jose Antonio Echeverria Havana Cuba IPN Ctr Invest Computac Mexico City Mexico IPN Ctr Innovac & Desarrollo Tecnol Computo Mexico City Mexico Univ Alicante Dept Comp Sci & Technol San Vicente Del Raspeig Spain

ISBN: (纸本)9783031755422;9783031755439

Intent recognition is a critical element of natural language understanding, as it enables AI systems and chatbots to understand the underlying goal or purpose of a user's input. There are several intent recognition solutions available, including Amazon Lex, DialogFlow, and RASA. RASA stands out among these solutions due to its customizable, open source nature and powerful deep learning integration. However, the official availability of RASA to the public presents some challenges that need to be addressed. This research proposes an API for detecting intent in text. The API is based on a RESTful service-oriented architecture style, uses the RASA framework as an intent detection solution, and leverages Docker container technology to simplify and accelerate API deployment and configuration. Unit tests were performed to validate the proposed solution, and the solution was deployed in the cloud using Docker. These unit tests not only validated the results, but also played a key role in improving the overall quality and efficiency of the solution. Deploying the solution in the cloud on a Linode server as a Docker container demonstrated that using Docker could facilitate its deployment and use.

关键词： intent recognition RASA RESTful API Docker

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A Dual-Loop Compensation Control Method to Improve Machining Accuracy for Robots with Secondary Encoders 17th

A Dual-Loop Compensation Control Method to Improve Machining...

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17th International Conference on Intelligent Robotics and Applications

作者： Peng, Wenxin Wu, Yuqiang Li, Changyou Hou, Che Chen, Wenjie Midea Grp State Key Lab High End Heavy Load Robots Foshan 528300 Peoples R China Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China Northeastern Univ Foshan Grad Innovat Sch Foshan 528312 Peoples R China Midea Corp Res Ctr Foshan 528311 Peoples R China

ISBN: (纸本)9789819607792;9789819607808

Large position tracking errors and vibrations are commonly seen in robot machining due to the robot's joint compliance. This paper proposes a dual-loop compensation control method to address this issue for robots with both motor and link side encoders. By taking advantage of link side encoders, the natural frequency and damping ratio of original robot dynamics are reshaped by a velocity compensation controller design to suppress vibrations. Meanwhile, link side position tracking error and velocity are employed to compute position compensation value to further improve robot machining accuracy. The effectiveness of this method was verified through single-joint simulation experiments. With large and noisy torques acting on the joint during robot machining, the simulated results showed that compared to full closed-loop P-PI control and ordinary damping control, the proposed method can significantly reduce the maximum position tracking error by up to 88.96% and 66.28% respectively with a shorter settling time.

关键词： Robot machining Position tracking accuracy Secondary encoder Dual-loop compensation

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Enhanced Knowledge Tracing via Frequency Integration and Order Sensitivity 21st

Enhanced Knowledge Tracing via Frequency Integration and Ord...

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21st Pacific Rim International Conference on artificial intelligence

作者： Cai, Songtao Li, Li Southwest Univ Sch Software Coll Comp & Informat Sci Chongqing Peoples R China

ISBN: (纸本)9789819601158;9789819601165

Knowledge tracing (KT) is a pivotal component of online education systems, aiming to assess and predict students' knowledge states based on their learning history. Existing knowledge tracing models have achieved considerable success, yet they have neglected the frequency of interactions between students and knowledge points. Frequency information can aid in more stable modeling of students' knowledge states. In this study, we incorporate frequency data into our model's question embeddings, enabling it to consider the frequency of student-knowledge concept interactions. Moreover, the order within students' learning sequences is crucial, but knowledge tracing models based on the transformer architecture, with their attention mechanisms, are insensitive to the sequence order. We propose the N-transformer structure that combines transformer with order-sensitive RNNs, effectively enhancing the model's sensitivity to the order in students' learning sequences. Subsequently, we employ a simpler and more effective method for prior knowledge modeling, directly extracting prior knowledge related to the current exercise from the model's predicted knowledge state at the previous time step. Finally, we have designed a decay function for the attention mechanism based on the Ebbinghaus forgetting curve to simulate the students' forgetting behavior. Ultimately, we conducted experiments on four datasets, Our model has achieved up to a 3% increase in AUC compared to the baseline on some datasets.

关键词： Knowledge tracing Deep learning Intelligent tutoring systems

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Event Camera Localization in 3D LiDAR Maps 17th

Event Camera Localization in 3D LiDAR Maps

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17th International Conference on Intelligent Robotics and Applications

作者： Cai, Ximeng Yu, Huai Wu, Ji Wang, Mingfeng Yu, Lei Xia, Gui-Song Wuhan Univ CAPTAIN Robot Lab Wuhan 430072 Peoples R China

ISBN: (纸本)9789819607884;9789819607891

Camera localization in prior LiDAR maps has gained more and more attention due to its lightweight and high precision. However, the well-developed RGB camera-based methods show severe performance degradation in high-speed motion and extreme imaging scenarios. To overcome this problem, we propose an efficient event camera-based localization method in prior 3D LiDAR maps, which utilizes the high dynamic range and microsecond resolution of event cameras to improve the localization accuracy in these challenging conditions. Specifically, we first employ an event-based visual odometry (VO) system to reconstruct local sparse 3D points from events. With the pose prediction from VO, we then match 3D points with maps using the nearest neighbor method to get coarse 3D matching points. Finally, the camera poses and 3D matching points are iteratively optimized by minimizing the distance error of the matching points. To verify the feasibility of the proposed approach, experiments are conducted on the public MVSEC and DSEC datasets. The results show that the proposed method accurately estimates the 6-DoF event camera pose under long trajectories and extreme conditions.

关键词： Camera localization Event camera Visual odometry

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Investigation on Grinding Temperature Evolutions and Surface Characteristics of CFRTP in Robotic Grinding 17th

Investigation on Grinding Temperature Evolutions and Surface...

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17th International Conference on Intelligent Robotics and Applications

作者： Yan, Xin Zhao, Huan Wang, Rongguo Peng, Zeyu Li, Pulin Ding, Han Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9789819607792;9789819607808

Robotic grinding has become an increasingly common technique for machining components with the high surface quality. Carbon fiber reinforced thermoplastic (CFRTP) components are often manufactured by near-net-shape forming and require robotic grinding to improve accuracy. In this study, the robot compliant grinding system was developed to achieve constant force grinding. The anisotropic temperature evolution of CFRTP were monitored by the infrared camera in real time. The effects of process parameters on grinding temperature characteristics and surface roughness for different materials were investigated. The surface quality of CFRTP can be effectively improved by reducing the robot feed rate, increasing the normal force and belt speed, and grinding at fibre angle of 90 degrees. This research aims to improve understanding of grinding temperature and surface roughness characteristics in robotic grinding to further providing technical guidance for the high-quality robotic grinding of CFRTP.

关键词： Robotic grinding Constant force control Grinding temperature monitoring Material removal Roughness

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