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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Wu, Yangyang Wang, Jun Miao, Xiaoye Wang, Wenjia Yin, Jianwei Software College Zhejiang University Ningbo China Academy of Interdisciplinary Studies The Hong Kong University of Science and Technology Hong Kong Hong Kong Center for Data Science Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Guangzhou China College of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9798350317152

The dramatically increasing volume of incomplete data makes the imputation models computationally infeasible in many real-life applications. In this paper, we propose an effective scalable imputation system named SCIS to significantly speed up the training of the differentiable generative adversarial imputation models under accuracy-guarantees for large-scale incomplete data. SCIS consists of two modules, differentiable imputation modeling (DIM) and sample size estimation (SSE). DIM leverages a new masking Sinkhorn divergence function to make an arbitrary generative adversarial imputation model differentiable, while for such a differentiable imputation model, SSE can estimate an appropriate sample size to ensure the user-specified imputation accuracy of the final model. Moreover, SCIS can also accelerate the autoencoder based imputation models. Extensive experiments upon several real-life large-scale datasets demonstrate that, our proposed system can accelerate the generative adversarial model training by 6.23x. Using around 1.27% samples, SCIS yields competitive accuracy with the state-of-the-art imputation methods in much shorter computation time. © 2024 IEEE.

关键词： Large datasets

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Thai Conversational Chatbot Classification Using BiLSTM and data Augmentation 1st

Thai Conversational Chatbot Classification Using BiLSTM and ...

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1st International Conference on data science and Artificial intelligence, DSAI 2023

作者： Lhasiw, Nunthawat Tanantong, Tanatorn Sanglerdsinlapachai, Nuttapong Thammasat Research Unit in Data Innovation and Artificial Intelligence Department of Computer Science Faculty of Science and Technology Thammasat University Pathum Thani Thailand Strategic Analytics Networks with Machine Learning and AI Research Team National Electronics and Computer Technology Center Pathum Thani Thailand

ISBN: (纸本)9789819979684

Chatbot platforms, e.g., Facebook and Line, have revolutionized human interaction in the digital age. In order to develop an automatic chatbot classification, there are several challenges especially for Thai chat messages. Conversational messages are usually short and ambiguous. Therefore, it is difficult to find a dataset for constructing an effective classification model. To address the limited size of the dataset, data augmentation techniques can be possibly applied. data augmentation involves generating synthetic messages by applying various transformations to existing data samples while preserving their original meaning. In this study, the size and diversity of the dataset is increased by two methods, i.e., text augmentation using word2vec from Thai2Fit and English-Thai machine translation models proposed by VISTEC. Based on the augmented messages, a Deep Learning technique, BiLSTM, is used to construct a chatbot classification model. The experimental obtained results demonstrate that data augmentation can help to increase the classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Classification (of information)

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On the Effectiveness of Heterogeneous Ensemble Methods for Re-identification

arXiv

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arXiv 2024年

作者： Klüttermann, Simon Rutinowski, Jérôme Nguyen, Anh Grimme, Britta Roidl, Moritz Müller, Emmanuel TU Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany Research Center Trustworthy Data Science and Security Germany

In this contribution, we introduce a novel ensemble method for the re-identification of industrial entities, using images of chipwood pallets and galvanized metal plates as dataset examples. Our algorithms replace commonly used, complex siamese neural networks with an ensemble of simplified, rudimentary models, providing wider applicability, especially in hardware-restricted scenarios. Each ensemble sub-model uses different types of extracted features of the given data as its input, allowing for the creation of effective ensembles in a fraction of the training duration needed for more complex state-of-the-art models. We reach state-of-the-art performance at our task, with a Rank-1 accuracy of over 77% and a Rank-10 accuracy of over 99%, and introduce five distinct feature extraction approaches, and study their combination using different ensemble methods. © 2024, CC BY.

关键词： Complex networks

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The map equation goes neural: mapping network flows with graph neural networks 24

The map equation goes neural: mapping network flows with gra...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Christopher Blöcker Chester Tan Ingo Scholtes Data Analytics Group Department of Informatics University of Zurich Switzerland Chair of Machine Learning for Complex Networks Center for Artificial Intelligence and Data Science Julius-Maximilians-Universität Würzburg Germany

ISBN: (纸本)9798331314385

Community detection is an essential tool for unsupervised data exploration and revealing the organisational structure of networked systems. With a long history in network science, community detection typically relies on objective functions, optimised with custom-tailored search algorithms, but often without leveraging recent advances in deep learning. Recently, first works have started incorporating such objectives into loss functions for deep graph clustering and pooling. We consider the map equation, a popular information-theoretic objective function for unsupervised community detection, and express it in differentiable tensor form for optimisation through gradient descent. Our formulation turns the map equation compatible with any neural network architecture, enables end-to-end learning, incorporates node features, and chooses the optimal number of clusters automatically, all without requiring explicit regularisation. Applied to unsupervised graph clustering tasks, we achieve competitive performance against state-of-the-art deep graph clustering baselines in synthetic and real-world datasets.

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Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling

Smaller Batches, Bigger Gains? Investigating the Impact of B...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Arthur Müller Felix Grumbach Matthia Sabatelli Department of Machine Intelligence Fraunhofer IOSB-INA Lemgo Germany Center for Applied Data Science Hochschule Bielefeld Gütersloh Germany Department of Artificial Intelligence University of Groningen Groningen The Netherlands

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Production scheduling is an essential task in manufacturing, with Reinforcement Learning (RL) emerging as a key solution. In a previous work, RL was utilized to solve an extended permutation flow shop scheduling problem (PFSSP) for a real-world production line with two stages, linked by a central buffer. The RL agent was trained to sequence equallysized product batches to minimize setup efforts and idle times. However, the substantial impact caused by varying the size of these product batches has not yet been explored. In this follow-up study, we investigate the effects of varying batch sizes, exploring both the quality of solutions and the training dynamics of the RL agent. The results demonstrate that it is possible to methodically identify reasonable boundaries for the batch size. These boundaries are determined on one side by the increasing sample complexity associated with smaller batch sizes, and on the other side by the decreasing flexibility of the agent when dealing with larger batch sizes. This provides the practitioner the ability to make an informed decision regarding the selection of an appropriate batch size. Moreover, we introduce and investigate two new curriculum learning strategies to enable the training with small batch sizes. The findings of this work offer the potential for application in several industrial use cases with comparable scheduling problems.

关键词： Training Home appliances Job shop scheduling Production Reinforcement learning Manufacturing Complexity theory Manufacturing automation

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FEDLWS: FEDERATED LEARNING WITH ADAPTIVE LAYER-WISE WEIGHT SHRINKING

arXiv

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arXiv 2025年

作者： Shi, Changlong Li, Jinmeng Zhao, He Guo, Dandan Chang, Yi School of Artificial Intelligence Jilin University China CSIRO’s Data61 Australia International Center of Future Science Jilin University China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE China

In Federated Learning (FL), weighted aggregation of local models is conducted to generate a new global model, and the aggregation weights are typically normalized to 1. A recent study identifies the global weight shrinking effect in FL, indicating an enhancement in the global model’s generalization when the sum of weights (i.e., the shrinking factor) is smaller than 1, where how to learn the shrinking factor becomes crucial. However, principled approaches to this solution have not been carefully studied from the adequate consideration of privacy concerns and layer-wise distinctions. To this end, we propose a novel model aggregation strategy, Federated Learning with Adaptive Layer-wise Weight Shrinking (FedLWS), which adaptively designs the shrinking factor in a layer-wise manner and avoids optimizing the shrinking factors on a proxy dataset. We initially explored the factors affecting the shrinking factor during the training process. Then we calculate the layer-wise shrinking factors by considering the distinctions among each layer of the global model. FedLWS can be easily incorporated with various existing methods due to its flexibility. Extensive experiments under diverse scenarios demonstrate the superiority of our method over several state-of-the-art approaches, providing a promising tool for enhancing the global model in FL. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Towards Development of Automated Knowledge Maps and databases for Materials Engineering using Large Language Models

arXiv

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arXiv 2024年

作者： Prasad, Deepak Pimpude, Mayur Alankar, Alankar Artificial Intelligence and Data Science Vivekanand Education Society Institute of Technology Mumbai India Department of Mechanical Engineering Indian Institute of Technology Bombay Mumbai400076 India Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay Mumbai400076 India

In this work a Large Language Model (LLM) based workflow is presented that utilizes OpenAI ChatGPT model GPT-3.5-turbo-1106 and Google Gemini Pro model to create summary of text, data and images from research articles. It is demonstrated that by using a series of processing, the key information can be arranged in tabular form and knowledge graphs to capture underlying concepts. Our method offers efficiency and comprehension, enabling researchers to extract insights more effectively. Evaluation based on a diverse Scientific Paper Collection demonstrates our approach in facilitating discovery of knowledge. This work contributes to accelerated material design by smart literature review. The method has been tested based on various qualitative and quantitative measures of gathered information. The ChatGPT model achieved an F1 score of 0.40 for an exact match (ROUGE-1, ROUGE-2) but an impressive 0.479 for a relaxed match (ROUGE-L ,ROUGE-Lsum) structural data format in performance evaluation. The Google Gemini Pro outperforms ChatGPT with an F1 score of 0.50 for an exact match and 0.63 for a relaxed match. This method facilitates high-throughput development of a database relevant to materials informatics. For demonstration, an example of data extraction and knowledge graph formation based on a manuscript about a titanium alloy is discussed. © 2024, CC BY-NC-ND.

关键词： Knowledge graph

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CBAM-SAUNet: A novel attention U-Net for effective segmentation of corner cases 46

CBAM-SAUNet: A novel attention U-Net for effective segmentat...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Rajamani, Srividya Tirunellai Rajamani, Kumar Angeline, J. Karthika, R. Schuller, Björn W. Embedded Intelligence for Health Care & Wellbeing University of Augsburg Germany Department of Artificial Intelligence Marwadi University Gujarat Rajkot India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Germany Munich Data Science Institute Germany Munich Center for Machine Learning GLAM-the Group on Language Audio & Music Imperial College London London United Kingdom

ISBN: (纸本)9798350371499

U-Net has been demonstrated to be effective for the task of medical image segmentation. Additionally, integrating attention mechanism into U-Net has been shown to yield significant benefits. The Shape Attentive U-Net (SAUNet) is one such recently proposed attention U-Net that also focuses on interpretability. Furthermore, recent research has focused on identification and reporting of corner cases in segmentation to accelerate the utilisation of deep learning models in clinical practise. However, achieving good model performance on such corner cases is a less-explored research area. In this paper, we propose CBAM-SAUNet which enhances the dual attention decoder block of SAUNet to improve its performance on corner cases. We achieve this by utilising a novel variant of the Convolutional Block Attention Module (CBAM)'s channel attention in the decoder block of SAUNet. We demonstrate the effectiveness of CBAM-SAUNet in the Automated Cardiac Diagnosis Challenge (ACDC) cardiac MRI segmentation challenge. Our proposed novel approach results in improvement in the Dice scores of 12% for Left Ventricle (LV) as well as Right Ventricle (RV) segmentation and 8% for Myocardium (MYO) for the identified corner-case dataset. © 2024 IEEE.

关键词： Image segmentation

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Can Language Models Serve as Temporal Knowledge Bases?

Can Language Models Serve as Temporal Knowledge Bases?

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhao, Ruilin Zhao, Feng Xu, Guandong Zhang, Sixiao Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Data Science and Machine Intelligence Lab University of Technology Sydney Sydney Australia

Recent progress regarding the use of language models (LMs) as knowledge bases (KBs) has shown that language models can act as structured knowledge bases for storing relational facts. However, most existing works only considered the LM-as-KB paradigm in a static setting, which ignores the analysis of temporal dynamics of world knowledge. Furthermore, a basic function of KBs, i.e., the ability to store conflicting information (i.e., 1-N, N-1 and N-M relations), is underexplored. In this paper, we formulate two practical requirements for treating LMs as temporal KBs: (i) the capacity to store temporally-scoped knowledge that contains conflicting information and (ii) the ability to use stored knowledge for temporally-scoped knowledge queries. We introduce a new dataset called LAMA-TK which is aimed at probing temporally-scoped knowledge, and investigate the two above requirements to explore the LM-as-KB paradigm in the temporal domain. On the one hand, experiments show that LMs can memorize millions of temporally-scoped facts with relatively high accuracy and transfer stored knowledge to temporal knowledge queries, thereby expanding the LM-as-KB paradigm to the temporal domain. On the other hand, we show that memorizing conflicting information, which has been neglected by previous works, is still challenging for LMs and hinders the memorization of other unrelated one-to-one relationships. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Reinforcement Learning as an Improvement Heuristic for Real-World Production Scheduling

Reinforcement Learning as an Improvement Heuristic for Real-...

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International Conference on machine Learning and Applications (ICMLA)

作者： Arthur Müller Lukas Vollenkemper Department of Machine Intelligence Fraunhofer IOSB-INA Lemgo Germany Center for Applied Data Science Bielefeld University of Applied Sciences and Arts Gütersloh Germany

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

The integration of Reinforcement Learning (RL) with heuristic methods is an emerging trend for solving optimization problems, which leverages RL's ability to learn from the data generated during the search process. One promising approach is to train an RL agent as an improvement heuristic, starting with a suboptimal solution that is iteratively improved by applying small changes. We apply this approach to a real-world multiobjective production scheduling problem. Our approach utilizes a network architecture that includes Transformer encoding to learn the relationships between jobs. Afterwards, a probability matrix is generated from which pairs of jobs are sampled and then swapped to improve the solution. We benchmarked our approach against other heuristics using real data from our industry partner, demonstrating its superior performance.

关键词： Industries Job shop scheduling Reinforcement learning Production Network architecture Transformers Search problems Market research Encoding Optimization

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