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Drawing Order Diagrams Through Two-Dimension Extension

Journal of Graph Algorithms and Applications 2023年第9期27卷 783-802页

作者： Dürrschnabel, Dominik Hanika, Tom Stumme, Gerd Knowledge & Data Engineering Group Research Center for Information System Design & Department of Electrical Engineering and Computer Science University of Kassel Germany Institute of Computer Science University of Hildesheim Germany Berlin School of Library and Information Science Humboldt-Universität zu Berlin Germany

Ordinal real-world data such as concept hierarchies, ontologies, genealogies, or task dependencies in scheduling often has the property to not only contain pairwise comparable, but also incomparable elements. Order diagrams provide an important tool for understanding and thus discovering knowledge in such data. Easily readable drawings of such order diagrams are hard to come by, even for small ordered sets. Many attempts were made to transfer classical graph drawing approaches to order diagrams. Although these methods produce satisfying results for some ordered sets, they unfortunately perform poorly in general. In this work, we present the novel algorithm DimDraw to decompose an ordered set (e.g., a concept hierarchy) in linear orders and to produce a corresponding order diagram. This algorithm is based on a relation between the dimension of an ordered set and the bipartiteness of its transitive incompatibility graph. To evaluate the quality of the algorithm, a user study was conducted where generated drawings were compared with ones from state-of-the-art drawing algorithms. © 2023 Brown University. All rights reserved.

关键词： Set theory

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study 9

Evaluating the Performance to Detecting Topic Shift of Large...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Tian, Pingfang Liu, Deting Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Institute of Scientific and Technical Information of China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Beijing100038 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan430065 China

ISBN: (纸本)9798350376548

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor. © 2024 IEEE.

关键词： adjacent utterances level ChatGPT contextual level single utterances level topic shift detection

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Reaction Network Simplification and Key Routes Extraction for Steam Cracking Process

SSRN

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SSRN 2023年

作者： Bi, Kexin Chen, Tujie Qiu, Tong Ji, Xu Dai, Yiyang School of Chemical Engineering Sichuan University Sichuan77840 China Department of Chemical Engineering Tsinghua University Beijing100084 China Beijing Key Laboratory of Industrial Big Data System and Application Tsinghua University Beijing100084 China

Reaction mechanism is critical for understanding complex reaction systems in specific production processes in industries such as petrochemical enterprises. In this study, we proposed a reaction network flow analysis - community based mechanism detection - target species selection (NFA-CMD-TSS) integrated framework for reaction network simplification and key routes extraction. Naphtha steam cracking reaction system was preliminary selected as an industrial example. The auto generated reaction network was simultaneously set as an input of PageRank-based reaction network analysis tool and community detection algorithm based on modularity analysis. After recording the importance and community affiliation information of each node in the network, the simplified communities were obtained and considered as skeletal mechanisms divided by reaction categories. Then TSS steps were implemented to search for the key nodes and key routes in these skeletal mechanisms. Through literature comparison and validation on an industrial case, the accuracy and trustworthiness of the results obtained by the framework have been confirmed. The output results including simplified reaction networks, detected communities, and key reaction routes were expected to bridge the gap between the microscale information and macroscale phenomena. © 2023, The Authors. All rights reserved.

关键词： Extraction

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Observation scheduling problem for AEOS with a comprehensive task clustering

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Journal of systems engineering and Electronics 2021年第2期32卷 347-364页

作者： CHANG Zhongxiang ZHOU Zhongbao YAO Feng LIU Xiaolu School of Business Administration Hunan UniversityChangsha 410082China Institute of Data Science and Decision Optimization Hunan UniversityChangsha 410082China Department of Mathematics Simon Fraser UniversitySurrey V3T 0A3Canada School of System Engineering National University of Defense TechnologyChangsha 410073China

Considering the flexible attitude maneuver and the narrow field of view of agile Earth observation satellite(AEOS)together,a comprehensive task clustering(CTC)is proposed to improve the observation scheduling problem for AEOS(OSPFAS).Since the observation scheduling problem for AEOS with comprehensive task clustering(OSWCTC)is a dynamic combination optimization problem,two optimization objectives,the loss rate(LR)of the image quality and the energy consumption(EC),are proposed to format OSWCTC as a bi-objective optimization *** the power of an adaptive large neighborhood search(ALNS)algorithm with a nondominated sorting genetic algorithm II(NSGA-II),a bi-objective optimization algorithm,ALNS+NSGA-II,is developed to solve *** on the existing instances,the efficiency of ALNS+NSGA-II is analyzed from several aspects,meanwhile,results of extensive computational experiments are presented which disclose that OSPFAS considering CTC produces superior outcomes.

关键词： observation scheduling comprehensive task clustering(CTC) bi-objective optimization image quality energy consumption(EC)

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An Explainable Artificial Intelligence Approach for Force Estimation from Surface-EMG sing the Element Description Method

An Explainable Artificial Intelligence Approach for Force Es...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Daiki Sodenaga Issei Takeuchi Daswin De Silva Seiichiro Katsura Department of System Design Engineering Keio University Yokohama Japan Abstech Co. Ltd. Saitama Japan Research Centre for Data Analytics and Cognition La Trobe University Melboulne Australia

In recent years, the need for technological advancement has been increasing because of the declining birthrate and aging populations. Motion copying is one such technical development that can effectively reproduce human motion. However, the challenges of conventional motion-copying systems include the lack of operability and loss of touch sensation at the abstraction phase. Then, we focused on the force estimation by surface-EMG for abstracting force information. In this paper, we address this challenge by developing an explainable Artificial Intelligence (XAI) approach that can estimate force using surface-EMG and uses element description method to interpret and identify the components contributing towards that estimation. We evaluated the proposed XAI method across several real-world experiments that confirm its value and contribution towards innovations in motion-copying system.

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Multilingual Zero Resource Speech Recognition Base on Self-Supervise Pre-Trained Acoustic Models

Multilingual Zero Resource Speech Recognition Base on Self-S...

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International Symposium on Chinese Spoken Language Processing

作者： Haoyu Wang Wei-Qiang Zhang Hongbin Suo Yulong Wan Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Data & AI Engineering System OPPO Beijing China

ISBN: (纸本)9798350397970

Labeled audio data is insufficient to build satisfying speech recognition systems for most of the languages in the world. There have been some zero-resource methods trying to perform phoneme or word-level speech recognition without labeled audio data of the target language, but the error rate of these methods is usually too high to be applied in real-world scenarios. Recently, the representation ability of self-supervise pre-trained models has been found to be extremely beneficial in zero-resource phoneme recognition. As far as we are concerned, this paper is the first attempt to extend the use of pretrained models into word-level zero-resource speech recognition. This is done by fine-tuning the pre-trained models on IPA phoneme transcriptions and decoding with a language model trained on extra texts. Experiments on Wav2vec2.0 and HuBERT models show that this method can achieve less than 20% word error rate on some languages, and the average error rate on 8 languages is 33.77%.

关键词： Training Error analysis Text recognition Speech recognition data models Acoustics Decoding

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CTGDiff: A Conditional Diffusion Model for Cardiotocography Signal Synthesis

CTGDiff: A Conditional Diffusion Model for Cardiotocography ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaoqing Li Pufan Cai Yu Lu Shijie Shi Liangkun Ma Xianghua Fu College of Big Data and Internet Shenzhen Technology University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Surgery Yong Loo Lin School of Medicine National University of Singapore Singapore School of Applied Technology Shenzhen University Shenzhen China Department of Obstetrics and Gynecology Peking Union Medical College Hospital Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The analysis of Cardiotocography (CTG) signals is often hindered by challenges such as limited data availability and label imbalance, which can undermine the performance of deep learning models. To address these issues, we present CTGDiff, a novel conditional diffusion model designed for generating synthetic Fetal Heart Rate (FHR) and Uterine Contraction (UC) signals. CTGDiff leverages both Phase-Rectified Signal Averaging (PRSA) spectrograms and UC as conditioning inputs for FHR, and integrates time encoding, condition generation from PRSA features, and residual blocks with dilated convolutions to capture both temporal dynamics and long-range dependencies. Extensive experiments, both qualitative and quantitative, demonstrate the model’s ability to synthesize high-quality CTG signals. In comparison with GANs and image-based diffusion models, CTGDiff achieves superior signal fidelity and distribution similarity for FHR, as indicated by metrics such as a 0.004 maximum mean deviation (MMD), 0.646 percent root mean square difference (PRD), 3.951 relative entropy (RE), and 0.291 Frechet distance (FD). Expert evaluations confirm that the model can generate both normal and abnormal CTG signals with high accuracy, conditioned on specific input data. These results underscore the potential of diffusion models for a wide range of applications in biomedical time series analysis, including signal synthesis, imputation, and noise reduction.

关键词： Measurement Fetal heart rate Biological system modeling Time series analysis Diffusion models data models Signal synthesis Cardiography Speech processing Spectrogram

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A GM-TCN based Hybrid Prediction Method for Ash Fouling Prediction in Coal-fired Power Plant Boiler

A GM-TCN based Hybrid Prediction Method for Ash Fouling Pred...

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2022 Chinese Automation Congress, CAC 2022

作者： Shi, Yuanhao Han, Tianxiang Cui, Fangshu Wen, Jie Qiao, Gangzhu North University of China School of Electrical and Control Engineering Taiyuan China Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China North University of China School of Data Science and Technology Taiyuan China

ISBN: (纸本)9781665465335

In response to the problem that ash deposition in boilers has a great impact on production efficiency as well as production safety, a grey level theory prediction model based on time convolution networks (GM-TCN) is proposed to predict the degree of ash in boiler heated areas by using a cleaning factor to assess the ash deposition condition on the heated surfaces. The accuracy of the prediction model is improved by utilizing the features of grey level theory that can predict the developmental changes in system behavior, combined with the high-precision approximation capability of the time-convolutional network. The modelling prediction of the ash level in the heated area of boilers during the production of coal-fired power stations is investigated. Experiments demonstrate that the model can predict the cleanliness factor with high accuracy, and the predicted values are closest to the true values compared to the other two models, verifying the accuracy and reliability of the model. © 2022 IEEE.

关键词： Convolution

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Secondary Reheat Steam Temperature Prediction Based on Hybrid Deep Learning 11

Secondary Reheat Steam Temperature Prediction Based on Hybri...

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11th IEEE data Driven Control and Learning systems Conference, DDCLS 2022

作者： Gao, YuQing Xue, Yali Sun, Li School of Energy and Environment Southeast University Jiangsu Nanjing210096 China State Key Lab of Power Systems Tsinghua University Department of Energy and Power Engineering Beijing100084 China Beijing Key Laboratory of Industrial Big Data System and Application Tsinghua University Beijing100084 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

An accurate prediction model of the secondary reheat steam temperature in the ultra-supercritical double-reheat coal-fired power plant is difficult to build due to the characteristics of strong nonlinearity, multi-variable coupling, large inertia, and various disturbances. This paper proposed a joint data-driven prediction method based on GRU-Pooling-Attention to improve the prediction accuracy and training speed. Firstly, CatBoost algorithm is taken to calculate the importance of features, and the input feature set is selected combined with expert knowledge. Then the GRU is adopted to learn the time series relationship between data, the pooling method is embedded to reduce the number of parameters and to highlight key values, and the attention mechanism is introduced to assign different weights to the output of each time step of GRU to reflect the large delay and large inertia of the system. The hybrid deep learning method is applied to the prediction of the secondary reheat steam temperature based on historical data, and the results show that it can obtain accurate and fast multi-step prediction. The trained model can be used to explore the dynamic characteristics of the processes. © 2022 IEEE.

关键词： Forecasting

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Last-X-Generation Archiving Strategy for Multi-Objective Evolutionary Algorithms

Last-X-Generation Archiving Strategy for Multi-Objective Evo...

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Congress on Evolutionary Computation

作者： Tianye Shu Yang Nan Ke Shang Hisao Ishibuchi Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

For evolutionary multi-objective optimization algorithms (EMOAs), an external archive can be utilized for saving good solutions found throughout the evolutionary process. Recent studies showed that a solution set selected from an external archive is usually superior to the final population. That is, the incorporation of an external archive improves the performance of EMOAs. However, the computation time for maintaining an external archive is long, especially when the archive size is large. To solve this issue, a simple archiving strategy is to save all solutions generated in the last several generations. In this paper, we examine this archiving strategy for three representative EMOAs on artificial test problems (Minus-DTLZ and WFG) and real-world problems (RE). Our results show that archiving the last several generations clearly improves the performance of EMOAs without severely increasing the computation time.

关键词： Costs Sociology Evolutionary computation Statistics Optimization Standards

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