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共 2812 条记录，以下是251-260 订阅

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OnDiscuss: An Epistemic Network Analysis learning Analytics Visualization Tool for Evaluating Asynchronous Online Discussions 6th

OnDiscuss: An Epistemic Network Analysis Learning Analytics ...

引用

6th international conference on Quantitative Ethnography, ICQE 2024

作者： Luther, Yanye Moraes, Marcia Ghosh, Sudipto Folkestad, James Department of Computer Science Colorado State University Fort CollinsCO80523 United States School of Education Colorado State University Fort CollinsCO80523 United States

ISBN: (纸本)9783031763342

Asynchronous online discussions are common assignments in both hybrid and online courses to promote critical thinking and collaboration among students. However, the evaluation of these assignments can require considerable time and effort from instructors. We created OnDiscuss, a learning analytics visualization tool for instructors that utilizes text mining algorithms and Epistemic Network Analysis (ENA) to generate visualizations of student discussion data. Text mining is used to generate an initial codebook for the instructor as well as automatically code the data. this tool allows instructors to edit their codebook and then dynamically view the resulting ENA networks for the entire class and individual students. through empirical investigation, we assess this tool’s effectiveness to help instructors in analyzing asynchronous online discussion assignments. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Maintenance 4.0 in mining Trucks: data Digitalization and Advanced Protocols 6

Maintenance 4.0 in Mining Trucks: Data Digitalization and Ad...

引用

6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Elkhenin, Nour Mrad, Hatem Rouyn-NorandaJ9X 5E4 Canada

Mobile mining equipment is essential but susceptible to costly failures. Despite the critical role of predictive maintenance in preventing such failures, existing research often lacks comprehensive frameworks that effectively integrate real-time telemetry data with advanced analytical tools to support decision-making in challenging mining environments. this study, conducted in collaboration with a mining partner, introduces a maintenance 4.0 approach utilizing telemetry data to enhance equipment maintenance and lifecycle management, with a focus on sustainable production. the methodology includes digitizing maintenance data and creating interactive dashboards with Power BI. machine learning and statistical models enable real-time analysis of telemetry data to detect early failure signs, improving predictive maintenance. Integrating these technologies enhances equipment availability, optimizes maintenance costs, and improves operator health and safety. this approach promotes a digital, intelligent supply chain, fostering efficient and sustainable operations and enhancing the reliability of mining activities in challenging environments. © 2024 the Authors. Published by Elsevier B.V.

关键词： Mine trucks

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Exploring Bias Analysis on Judicial data using machine learning Techniques 12

Exploring Bias Analysis on Judicial Data using Machine Learn...

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12th international conference on pattern recognition Systems (ICPRS)

作者： Silva, Bruno Santos F. Costa-Abreu, Marjory Univ Fed Rio Grande do Norte PPgSC DIMAP Natal RN Brazil Sheffield Hallam Univ Comp Sci Sheffield S Yorkshire England

ISBN: (数字)9781665466943

ISBN: (纸本)9781665466943

the use of data driven automation is not new, but it has gain a lot of attention recently with the wide-spread understanding that it is the solution to all problems in terms of 'fair' and 'non-bias' classification. this is not different in the law area, where 'artificial intelligence' became a 'magic word'. However, using historic data is a very tricky job which can quite easily propagate discrimination in a very efficient way. thus, this work is aimed to analyse data from legal proceedings looking for evidence related to the occurrence of bias in the judges' decision-making process, considering mainly the gender or social condition of the convicts. Supervised and unsupervised machine learning techniques, preceded by data analysis and processing procedures, were used to explain and find explicit data behaviour. Our results pointed to the fragility of the techniques to identify biases but suggest the need to improve data pre-processing and the search for more robust classification techniques.

关键词： bias data mining machine learning unsupervised learning supervised learning

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Cascade Temporal Convolutional Network for Multitask learning 6

Cascade Temporal Convolutional Network for Multitask Learnin...

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6th international conference on Artificial Intelligence and Big data, ICAIBD 2023

作者： Zhang, Huiyun Huang, Heming Han, Henry Baylor University School of Engineering and Computer Science Department of Computer Science United States Qinghai Normal University School of Computer Science Xining China Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application School of Computer Science Xining China

ISBN: (纸本)9781665491259

Multi-task learning (MTL) is a powerful technique in machine learning that enables the simultaneous training of multiple tasks. In this paper, we propose a novel MTL approach for the recognition of emotions, speakers, and gender in speech signals. Our approach presents a novel deep learning model called the Cascade Temporal Convolutional Network (CTCN to handle MTL efficiently. CTCN is designed to effectively improve the parallel processing ability of the model in multitasking learning. It provides an effective solution for improving the performance of deep learning in multitasking scenarios. CTCN is particularly adept at processing sequences of data with varying lengths and extracting features that are relevant to multiple tasks. Our experimental results demonstrate that the proposed CTCN approach for MTL achieves significantly superior performance on all tasks on benchmark datasets compared to other deep learning models. this study will inspire more future work in the MTL field. © 2023 IEEE.

关键词： Speech recognition

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Enforcement of DNN with LDA-PCA-ELM for PIE Invariant Few-Shot Face recognition 10th

Enforcement of DNN with LDA-PCA-ELM for PIE Invariant Few-Sh...

引用

10th Biennial international conference on pattern recognition and machine Intelligence (PReMI)

作者： Rai, Anvaya Lall, Brejesh Zalani, Astha Prakash, Raghwender Srivastava, Shikha Ctr Dev Telemat New Delhi India Indian Inst Technol Delhi New Delhi India

ISBN: (纸本)9783031451690;9783031451706

Face recognition has been an active area of research but limited work has been done in the domains where the captured face images are of very low resolution, blurred and occluded. the other challenges in the domains like face recognition at a distance or unconstrained face recognition, include pose and expression variations. Additionally, for use cases like person re-identification and surveillance, the availability of limited amount of annotated data for various classes, makes it a highly probable candidate for Few-shot face recognition. In this work, we present a deep learning architecture that uses Deep Neural Networks (DNN) along with Extreme learning machines (ELM) to address the problem of generating compact and discriminating face representations, while accounting for the few-shot constraints of the novel dataset. We have used three types of ELMs namely, PCA-ELM, LDA-ELM and LDA-PCA-ELM, to augment the DNN and achieve better accuracy with lower computational cost at the time of inference.

关键词： Low-shot recognition Dimensionality Reduction Hyperspherical Manifold Extreme learning machines (ELM) Principal Component Analysis Extreme learning machine (PCA-ELM) Linear Discriminant Analysis Extreme learning machine (LDA-ELM)

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Design and Implementation of a machine learning Model for Soccer Match Prediction Based on Player Statistics 17th

Design and Implementation of a Machine Learning Model for So...

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17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and Applications, CIAPP 2024, held in conjunction with Mexican international conference on Artificial Intelligence, MICAI 2024

作者： Barrientos, Antonio Muñoz Bautista, Humberto Muñoz Díaz, Alejandro Padilla Rodríguez, Francisco Javier Álvarez Universidad Autónoma de Aguascalientes AGS Aguascalientes20100 Mexico

ISBN: (纸本)9783031838781

Football generates vast amounts of data daily on players, teams, and leagues, used for performance analysis. However, advanced analysis technologies are often inaccessible to lower-budget teams. this study developed a predictive tool to estimate the number of goals a team might score in a match, focusing on the UEFA Champions League with data from 2005 to 2022, totaling 4237 records. the model development included three stages: database creation, data preprocessing and analysis, and model construction. data from the Fbref and FIFA Index, including player data from the EA FC video game, were collected using web scraping to build an expandable database. Additional variables, such as team origin league and momentum (average goals in the last five matches), were added to improve model accuracy. the model, a deep fully connected neural network evolved from a multi-layer perceptron, achieved 57% accuracy. the findings suggest that effective analysis tools can be created using data mining and machine learning, supporting lower-budget teams by providing more information and reducing competitive disparities. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Budget control

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Literature review and process development for inbound transport demand forecasts 6

Literature review and process development for inbound transp...

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6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Jacobsen, Jens Jakob Mardanova, Marina Moroff, Nikolas Ulrich Fraunhofer Institute for Material Flow and Logistics Joseph-von-Fraunhofer-Str. 2-4 Dortmund44227 Germany

Order dates and delivery times in the inbound-logistic are subject to extern influences. the resulting fluctuations are imposing a challenge for the planning of the necessary transport and handling capacities. In order to plan in an environment of uncertain demands, the method of demand forecasting provides an approach to improve the planning accuracy. Due to the high volatility of the underlying demand time series machine learning (ML) based methods are identified to enable time series predictions of transport demands. For the identification of suitable ML methods, a representative and structured literature review is conducted. Following the analyses of the review an approach for the implementation of ML models in a data mining use case for transport demand predictions is presented. To ensure the actual implementation of the identified Long-short-term-memory (LSTM) model a process model based on the Cross-Industry-Standard-Process for data mining (CRISP-DM) is formulated to support the implementation with specific steps required for this use case. With this approach, a consistent concept is created which enables the selection and implementation of a time series forecast for transport demands and thus the optimization of transport demand planning. © 2025 the Authors. Published by Elsevier B.V.

关键词： Inbound Logistics LSTM machine learning Process Model Transport Forecast

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Prompt learning for Subjective and Objective recognition of Text 6

Prompt Learning for Subjective and Objective Recognition of ...

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6th IEEE international conference on Computer and Communication Engineering Technology, CCET 2023

作者： Cheng, Baoping Lu, Shaoshuai Duan, Yiping Wang, Sheng Zhang, Qinlong Information Technology Co. Ltd Department of Electronic Engineering Beijing China Information Technology Co. Ltd. Dpartment of Converged Communication Systems Hangzhou China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798350340686

Subjective expressions generally contain the user's emotional opinions and thus can better represent the sentiment information of the originating text reviews, while objective descriptions usually appear in declarative sentences that do not reflect the sentiment polarity of the comment text. therefore, subjective and objective recognition techniques for text are necessary for natural language processing tasks (e.g. opinion mining, sentiment analysis). However, most existing deep learning-based methods need large human-annotated data, but labeling large amounts of high-quality emotional texts is labor-intensive. the lack of labeled data has become a bottleneck restricting the deep algorithm. To cope with the shortage of labeled data, we employ Prompt learning strategy in Subjective and Objective recognition task of text, thus proposing a simple PLSO method. PLSO can effectively inherit the abundant prior knowledge from the pre-trained model and learn high-level semantic features of text by reducing the differences between upstream and downstream tasks. Meanwhile, we introduce triplet loss as a regular term in the training objective, which guides the model to capture the category distribution of data in joint training. Benefiting from the above advantages, the PLSO approach significantly outperforms the other baseline methods in the data sparsity scenario, demonstrating its superiority and effectiveness. © 2023 IEEE.

关键词： Deep learning

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Contrastive Alignment for Deep Multi-view Subspace Clustering 6

Contrastive Alignment for Deep Multi-view Subspace Clusterin...

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Wang, Jiao Wu, Bin Zhang, Hongying Zhou, Yunhui Southwest University of Science and Technology School of Information Engineering Mianyang China

ISBN: (纸本)9798350325485

Deep multi-view subspace clustering can partition non-linear high-dimensional data by exploring a consensus self-expression among the samples and has attracted increasing attention. However, most existing methods learn the consensus self-expression by fusing view-specific self-expression, where they neglect that view-private meaningless information or noise may interfere the learning of consensus self-expression. In this paper, we propose a novel Contrastive Alignment for Deep Multi-view Subspace Clustering (CADMVSC) method. Specifically, the view-specific self-expression are obtained by self-expressive layer embedded in autoencoder network for each view. To guarantee the consistency among the views and reduce the interference of view-private information, we align the view-specific self-expression by contrastive learning. Moreover, all the aligned self-expression are fused adaptively to obtain the consensus self-expression by channel attention mechanism, which can fully explore the complementarity of views. Extensive experiments have shown the excellent clustering performance of our method over other state-of-the-Art methods. © 2023 IEEE.

关键词： Deep learning

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Automatic Berthing Based on Reinforcement learning and Feedback Control 6

Automatic Berthing Based on Reinforcement Learning and Feedb...

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Ding, Kaiming Ye, Jun Hu, Yi Dang, Jinglong Liu, Jialun Ocean and Energy Power Engineering Wuhan University of Technology School of Naval Architecture WuHan430063 China Wuhan University of Technology State Key Laboratory of Maritime Technology and Safety Intelligent Transportation Systems Research Center WuHan430063 China

ISBN: (纸本)9798350325485

Berthing is one of the most critical and complex operations for ships. Application of automatic berthing control can improve the safety, efficiency, and sustainability of vessel maneuvering. Current studies on automatic berthing control systems are mostly based on optimal control theory or artificial neural networks. these control methods either have low environmental adaptability or need large amounts of data to support the training process. In this paper, a novel method for automatic ship berthing that combines reinforcement learning with feedback control is proposed. Such method can adapt to different ship and navigation environments by automatically adjusting control parameters. this simplifies the controller deployment on board and reduces the data requirement for agent training. Simulations show that the proposed methods could handle auto-berthing problem in various scenarios. © 2023 IEEE.

关键词： Reinforcement learning

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