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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Junwei Lv Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Early classification of time series aims to accurately predict the class label of a time series as early as possible, which is significant but challenging in many time-sensitive applications. Existing early classification methods hold a basic closed-world assumption that the classifier must have seen the classes of test samples. However, new samples that do not belong to any trained class may appear in the real world. In this paper, we first address the early classification in an open world and design two detectors to identify which known class or unknown class a sample belongs to. Specifically, based on the observed data, an early known-class detector is designed to determine the known-class confidence and an early unknown-class detector is designed to determine the unknown-class confidence according to the Minimum Reliable Length (MRL) and the Weibull distribution of each class. Experimental results evaluated on real-world datasets demonstrate that the proposed model can identify samples of unknown and known classes accurately and early.

关键词： Computational modeling Conferences Time series analysis Detectors Reliability engineering Software Weibull distribution

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Co-training Based on Semi-Supervised Ensemble Classification Approach for Multi-label data Stream

Co-training Based on Semi-Supervised Ensemble Classification...

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IEEE International Conference on Big knowledge (ICBK)

作者： Zhe Chu Peipei Li Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education AnHui China School of Computer Science and Information Engineering Hefei University of Technology AnHui China

A large amount of data streams in the form of texts and images has been emerging in many real-world applications. These data streams often present the characteristics such as multi-labels, label missing and new class emerging, which makes the existing data stream classification algorithm face the challenges in precision space and time performance. This is because, on the one hand, it is known that data stream classification algorithms are mostly trained on all labeled single-class data, while there are a large amount of unlabeled data and few labeled data due to it is difficult to obtain labels in the real world. On the other hand, many of existing multi-label data stream classification algorithms mostly focused on the classification with all labeled data and without emerging new classes, and there are few semi-supervised methods. Therefore, this paper proposes a semi-supervised ensemble classification algorithm for multi-label data streams based on co-training. Firstly, the algorithm uses the sliding window mechanism to partition the data stream into data chunks. On the former w data chucks, the multi-label semi-supervised classification algorithm COINS based on co-training is used to training a base classifier on each chunk, and then an ensemble model with w COINS classifiers is generated ensemble model to adapt to the environment of data stream with a large number of unlabeled data. Meanwhile, a new class emerging detection mechanism is introduced, and the w+1 data chunk is predicted by the ensemble model to detect whether there is a new class emerging. When a new label is detected, the classifier is retrained on the current data chunk, and the ensemble model is updated. Finally, experimental results on five real data sets show that: as compared with the classical algorithms, the proposed approach can improve the classification accuracy of multi-label data streams with a large number of missing labels and new labels emerging.

关键词： data models Training Streaming media Adaptation models Partitioning algorithms Correlation knowledge engineering

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TransD-based Multi-hop Meta Learning for Few-shot knowledge Graph Completion

TransD-based Multi-hop Meta Learning for Few-shot Knowledge ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jindi Li Kui Yu Yuling Li Yuhong Zhang Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Few-shot knowledge graph completion (FKGC), which aims to infer missing facts about a relation from only a few reference triples, has recently attracted great attention. The core of solving the FKGC task is to learn a vector representation for each few-shot relation using the corresponding entity represen-tations. To this end, existing models generally enhance entity representations with their direct neighbors. However, a large number of entities have few direct neighbors. Hence, encoding only direct neighborhood is insufficient to obtain satisfactory en-tity representations. In addition, current models typically utilize static embeddings to represent entities, ignoring their diverse semantics, i.e., an entity may show distinct semantics within different few-shot relations. To address these issues, we propose a new FKGC framework, namely TransD-based Multi-hop Meta Learning (TDML). TDML consists of three main components: a multi-hop neighbor encoder to enhance entity representations by aggregating heterogeneous multi-hop neighbors, a transformer encoder to generate the relation meta representations, and a TransD-based relation representation updater that allows each entity to exhibit relation-specific semantics and tune the relation meta representations. Extensive experiments on two public datasets demonstrate that our model outperforms state-of-the-art FKGC methods.

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Classification of Table Cells Based on LLM Prompts

Classification of Table Cells Based on LLM Prompts

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IEEE International Conference on Systems, Man and Cybernetics

作者： Mengjie Liu Chenyang Bu Shengxing Bai Bingbing Dong Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Tables, as an important means of data storage, are widely used in spreadsheets, web tables, and PDFs. By integrating information from table data with knowledge re-trieved from an external knowledge base, and examining the correspondences between cell values in the table and instances in the knowledge base, we can extract knowledge from the table to augment and enrich the knowledge base. To achieve this goal, we first need to classify table cells based on their functions in the layout. Due to the diverse structures arising from the arrangements of rows and columns, as well as the complexity of content resulting from concise data storage, current automation techniques heavily rely on stylistic features of table cells, such as font or color. Moreover, these methods are rarely experimented with or validated on tables without style features. Recent literature indicates that large language models (LLMs) demonstrate an ability to understand the structure and content of tables in tasks such as table judgment reasoning. Even without extensive feature inputs or pre-training, LLMs still show comparable results to machine learning and deep learning in these tasks. Therefore, this paper attempts to apply LLMs to table cell classification without using other stylistic features. We have designed a 4-component prompt paradigm (Classification Definition, Instruction, Table, Com-pletion), representing respectively the classification definition, task instructions, table data, and result output. We conduct experiments on three datasets CIUS, SAUS, and DEEX for table cell classification with one-shot learning. Our experimental results show that with the assistance of LLMs, better results can be achieved without utilizing stylistic features.

关键词： Deep learning Image color analysis Large language models knowledge based systems Layout Memory One shot learning Benchmark testing Probability density function data mining

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Research on Multi-AGVs dynamic scheduling based on deep reinforcement learning 19

Research on Multi-AGVs dynamic scheduling based on deep rein...

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19th IEEE International Conference on e-Business engineering, ICEBE 2023

作者： Qin, Zhuorui Wu, Xiaoqian Zheng, Yimin Wu, Qingyao South China University of Technology School of Software Engineering Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Shenzhen Bangqi Technology Intelligent Development Co. Ltd Shenzhen China Pazhou Lab Guangzhou China

ISBN: (纸本)9798350325553

Driven by the latest advances in industrial artificial intelligence, automatic guided vehicles (AGVs) have been widely used for material handling in flexible workshops. In the pursuit of agile and flexible production transportation planning, the ability to handle unpredictable and dynamic events is becoming increasingly important. In order to achieve this real-time dynamic control, this study proposes a distributed multi-agent architecture to solve the dynamic Multi-AGVs scheduling problem. Double Deep Q-Network algorithm is used to train the scheduling agent, capture the relationship between production transportation operation information and scheduling goals, and make real-time scheduling decisions for flexible jobs with constant arrival intervals. Specialized state, action, and reward representations are proposed to handle variable specifications of dynamic scheduling problems. The performance of this method under different scene scales was verified through simulation studies. Numerical results show that compared with existing traditional heuristic scheduling strategies, this method not only has superior performance, but also its advantages persist even if the manufacturing system job scale changes. © 2023 IEEE.

关键词： Reinforcement learning

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Fine-grained Cross-modal Fusion based Refinement for Text-to-Image Synthesis

arXiv

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arXiv 2023年

作者： Haoran, Sun Yang, Wang Haipeng, Liu Biao, Qian Meng, Wang Key Laboratory of Knowledge Engineering With Big Data Ministry of Education Hefei University of Technology Hefei China Department of Computer Science and Information Engineering Hefei University of Technology Hefei China

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel Fine-grained text-image Fusion based Generative Adversarial Networks, dubbed FF-GAN, which consists of two modules: Fine-grained text-image Fusion Block (FF-Block) and Global Semantic Refinement (GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given *** is available at htt s:// ithub com/haoranhfut/FF-GAN Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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More Grounded Image Captioning by Distilling Image-Text Matching Model

More Grounded Image Captioning by Distilling Image-Text Matc...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuanen Zhou Meng Wang Daqing Liu Zhenzhen Hu Hanwang Zhang Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) School of Computer Science and Information Engineering Hefei University of Technology University of Science and Technology of China Nanyang Technological University

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Visual attention not only improves the performance of image captioners, but also serves as a visual interpretation to qualitatively measure the caption rationality and model transparency. Specifically, we expect that a captioner can fix its attentive gaze on the correct objects while generating the corresponding words. This ability is also known as grounded image captioning. However, the grounding accuracy of existing captioners is far from *** improve the grounding accuracy while retaining the captioning quality, it is expensive to collect the word-region alignment as strong *** this end, we propose a Part-of-Speech (POS) enhanced image-text matching model (SCAN[24]): POS-SCAN, as the effective knowledge distillation for more grounded image captioning. The benefits are two-fold: 1) given a sentence and an image, POS-SCAN can ground the objects more accurately than SCAN; 2) POS-SCAN serves as a word-region alignment regularization for the captioner's visual attention module. By showing benchmark experimental results, we demonstrate that conventional image captioners equipped with POS-SCAN can significantly improve the grounding accuracy without strong supervision. Last but not the least, we explore the indispensable Self-Critical Sequence Training (SCST[46]) in the context of grounded image captioning and show that the image-text matching score can serve as a reward for more grounded captioning 1 .

关键词： Visualization Grounding Task analysis Training Measurement Computational modeling Image edge detection

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Optimizing parameters of algorithm trading strategies using MapReduce

Optimizing parameters of algorithm trading strategies using ...

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： Xiongpai Qin Huiju Wang Furong Li Jidong Chen Xuan Zhou Xiaoyong Du Shan Wang Key Laboratory of Data Engineering and Knowledge Engineering (RUC) Ministry of Education Beijing China School of Information Renmin University of China Beijing China EMC-Greenplum Research China China

In algorithm trading, computer algorithms are used to make the decision on the time, quantity, and direction of operations (buy, sell, or hold) automatically. To create a useful algorithm, the parameters of the algorithm should be optimized based on historical data. However, Parameter optimization is a time consuming task, due to the large search space. We propose to search the parameter combination space using the MapReduce framework, with the expectation that runtime of optimization be cut down by leveraging the parallel processing capability of MapReduce. This paper presents the details of our method and some experiment results to demonstrate its efficiency. We also show that a rule based strategy after being optimized performs better in terms of stability than the one whose parameters are arbitrarily preset, while making a comparable profit.

关键词： Optimization Measurement Computers Algorithm design and analysis Runtime Servers data processing

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A Client Selection Method Based on Loss Function Optimization for Federated Learning

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Computer Modeling in engineering & Sciences 2023年第10期137卷 1047-1064页

作者： Yan Zeng Siyuan Teng Tian Xiang Jilin Zhang Yuankai Mu Yongjian Ren Jian Wan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Key Laboratory of Complex System Modeling and Simulation Ministry of EducationHangzhou310018China Zhejiang Engineering Research Center of Data Security Governance Hangzhou310018China Intelligent Robotics Research Center Zhejiang LabHangzhou311100China HDU-ITMO Joint Institute Hangzhou Dianzi UniversityHangzhou310018China

Federated learning is a distributedmachine learningmethod that can solve the increasingly serious problemof data islands and user data privacy,as it allows training data to be kept locally and not shared with other *** trains a globalmodel by aggregating locally-computedmodels of clients rather than their ***,the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global *** this problem,we focus on the client selection with federated learning,which can affect the convergence performance of the global model with the selected local *** propose FedChoice,a client selection method based on loss function optimization,to select appropriate local models to improve the convergence of the global *** firstly sets selected probability for clients with the value of loss function,and the client with high loss will be set higher selected probability,which can make them more likely to participate in ***,it introduces a local control vector and a global control vector to predict the local gradient direction and global gradient direction,respectively,and calculates the gradient correction vector to correct the gradient direction to reduce the cumulative deviationof the local gradient causedby *** experiments to verify the validity of FedChoice on CIFAR-10,CINIC-10,MNIST,EMNITS,and FEMNIST datasets,and the results show that the convergence of FedChoice is significantly improved,compared with FedAvg,FedProx,and FedNova.

关键词： Federated learning model aggregation Non-IID

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Discovering Reliable Information Extraction Patterns with Pre-Trained Model for Text with Writing Style

Discovering Reliable Information Extraction Patterns with Pr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chenyang Bu Jiacheng Liu Jiaxuan Liu Shengwei Ji Hongbin Yang Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China School of Artificial Intelligence and Big Data Hefei University China

Large-scale pre-trained models such as GPT and BERT have demonstrated remarkable performance in information extraction tasks. However, their black-box nature poses challenges for reliability and interpretability. In contrast, rule- based extraction methods have better interpretability, but typically require domain experts to manually establish rules, limiting their generalization ability. In industry, there is often a demand for reliable knowledge extraction to reduce the time spent on manual verification of each piece of knowledge. In this paper, we explore the idea of combining GPT and symbolic-based methods to automatically discover reliable extraction patterns in text with a particular writing style. This method leverages the characteristics of high information density and similar writing patterns in text with a specific writing style to generate verifiable and reliable patterns. We conduct experiments on two datasets with a specific writing style to demonstrate its effectiveness, validating the idea of combining large models for reliable information extraction pattern discovery in the tested datasets.

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