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17th Chinese Intelligent Systems Conference, CISC 2021

作者： Ma, Zeqi Ma, Lingwei Fu, Dongmei Song, Guangxuan Zhang, Dawei Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing100083 China National Materials Corrosion and Protection Data Center University of Science and Technology Beijing Beijing100083 China Shunde Graduate School of University of Science and Technology Beijing Fo Shan528300 China

ISBN: (纸本)9789811663192

Knowledge graph, as a semantic network, provides a method to model knowledge from the perspective of entities and their relationships. Since a large quantity of material knowledge is widely distributed, using the structure of the knowledge graph is an effective approach to clarify the internal connections between related knowledge. Named entity recognition is a crucial step to construct a knowledge graph. However, most of the professional domain datasets are label-free and collected from multiple heterogeneous sources. To identify the entities from these datasets, a rule-based and deep learning hybrid method is proposed. The knowledge extracted by regular expression was used to semi-automatically label the corpus, and then trained by deep learning model, which achieved good results. The constructed knowledge graph regarding to the commonly used steels is of great significance to the integration and management of related knowledge, and it lays a foundation for future engineering application. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Knowledge graph

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Construction of Visual Question and Answering System Based on Knowledge Graph for Specific Objects 17th

Construction of Visual Question and Answering System Based o...

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17th Chinese Intelligent Systems Conference, CISC 2021

作者： Dai, Jiaxin Ma, Lingwei Fu, Dongmei Zhang, Dawei Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing100083 China National Materials Corrosion and Protection Data Center University of Science and Technology Beijing Beijing100083 China Shunde Graduate School of University of Science and Technology Beijing Fo Shan528300 China

ISBN: (纸本)9789811663192

As China’s industry enters the 4.0 digital era, the segmentation of industrial applications is diversified. The knowledge of specific objects in the industrial scene is scattered that brings the pain points for management and operation. Knowledge graph (KG) has a powerful ability for relationship expression and real-time feedback that can integrate multi-source heterogeneous knowledge and provide data support for the Question Answering (Q&A) system. In this paper, a semi-automatic construction scheme for cupping machine knowledge graph is proposed and a visual Q&A system based on KG by the rule-based method is constructed. In the implementation of the KG, we design an ontology schema of the cupping machine and obtain entities and relationships from the network. This work solves the problem of the data island of cupping machines and provides a new technical method for the knowledge management of specific objects in the industrial equipment field. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Knowledge graph

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DG-CNN:Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

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Journal of Computer science & Technology 2022年第2期37卷 277-294页

作者： Xiao-Zheng Xie Jian-Wei Niu Senior Member Xue-Feng Liu Qing-Feng Li Yong Wang Jie Han Shaojie Tang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityHangzhou 310051China Department of Diagnostic Ultrasound National Cancer CenterChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100021China Naveen Jindal School of Management The University of Texas at DallasRichardsonTX 75080-3021U.S.A.

Although using convolutional neural networks(CNNs)for computer-aided diagnosis(CAD)has made tremendous progress in the last few years,the small medical datasets remain to be the major bottleneck in this *** address this problem,researchers start looking for information out of the medical *** efforts mainly leverage information from natural images via transfer *** recent research work focuses on integrating knowledge from medical practitioners,either letting networks resemble how practitioners are trained,how they view images,or using extra *** this paper,we propose a scheme named Domain Guided-CNN(DG-CNN)to incorporate the margin information,a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound(BUS)*** DG-CNN,attention maps that highlight margin areas of tumors are first generated,and then incorporated via different approaches into the *** have tested the performance of DG-CNN on our own dataset(including 1485 ultrasound images)and on a public *** results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their *** example,experimental results on our dataset show that with a certain integrating mode,the improvement of using DG-CNN over a baseline network structure ResNet 18 is 2.17%in accuracy,1.69%in sensitivity,2.64%in specificity and 2.57%in AUC(Area Under Curve).To the best of our knowledge,this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

关键词： medical consensus domain knowledge breast cancer diagnosis margin map deep neural network

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Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models 30

Step-by-Step: Controlling Arbitrary Style in Text with Large...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Pusheng Wu, Lianwei Wang, Linyong Guo, Sensen Liu, Yang National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China Research & Development Institute of Northwestern Polytechnical University Shenzhen China Chongqing Science & Technology Innovation Center of Northwestern Polytechnical University China State Key Laboratory of Integrated Service Networks Xidian University China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University China

ISBN: (纸本)9782493814104

Recently, the autoregressive framework based on large language models (LLMs) has achieved excellent performance in controlling the generated text to adhere to the required style. These methods guide LLMs through prompt learning to generate target text in an autoregressive manner. However, this manner possesses lower controllability and suffers from the challenge of accumulating errors, where early prediction inaccuracies might influence subsequent word generation. Furthermore, existing prompt-based methods overlook specific region editing, resulting in a deficiency of localized control over input text. To overcome these challenges, we propose a novel three-stage prompt-based approach for specific region editing. To alleviate the issue of accumulating errors, we transform the text style transfer task into a text infilling task, guiding the LLMs to modify only a small portion of text within the editing region to achieve style transfer, thus reducing the number of autoregressive iterations. To achieve an effective specific editing region, we adopt both prompt-based and word frequency-based strategies for region selection, subsequently employing a discriminator to validate the efficacy of the selected region. Experiments conducted on several publicly competitive datasets for text style transfer task confirm that our proposed approach achieves state-of-the-art performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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Efficient Pooling Designs and Screening Performance in Group Testing for Two Types of Defectives

Efficient Pooling Designs and Screening Performance in Group...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Hiroyasu Matsushima Yusuke Tajima Xiao-Nan Lu Masakazu Jimbo Data Science and AI Innovation Reseach Promotion Center Shiga University Hikone Japan Department of Electrical Electronic and Computer Engineering Gifu University Gifu Japan Center for Training Professors in Statistics The Institute of Statistical Mathematics Tokyo Japan

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Group testing is utilized in the case when we want to find a few defectives among a large amount of items. Testing $n$ items one by one requires $n$ tests, but if the ratio of defectives is small, group testing is an efficient way to reduce the number of tests. Many researches have been developed for group testing for a single type of defectives. In this paper, we consider the case where two types of defective A and B exist. For two types of defectives, we develop a belief propagation algorithm to compute the marginal posterior probability of defectives. Furthermore, we construct several kinds of collections of pools in order to test for A and B. And, by utilizing our belief propagation algorithm, we evaluate the performance of group testing by conducting simulations.

关键词： Geometry Computational modeling Posterior probability Software algorithms Software Testing Belief propagation

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CT perfusion parameter estimation in stroke using neural network with transformer and physical model priors

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Computers in Biology and Medicine 2024年 182卷 109134-109134页

作者： Luo, Luyao Liu, Pan Ye, Wanxing Chen, Fengwei Liu, Yu Liu, Ziyang Jing, Jing Xiong, Yunyun Zhu, Wanlin Jiang, Yong Cheng, Jian Wang, Yongjun Liu, Tao Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China Medical Big Data Research Center Chinese PLA General Hospital Beijing China China National Clinical Research Center for Neurological Diseases Beijing China Department of Neurology Beijing Tiantan Hospital Capital Medical University Beijing China School of Computer Science and Engineering Beihang University Beijing China

Objectives: CT perfusion (CTP) imaging is vital in treating acute ischemic stroke by identifying salvageable tissue and the infarcted core. CTP images allow quantitative estimation of CT perfusion parameters, which can provide information on the degree of tissue hypoperfusion and its salvage potential. Traditional methods for estimating perfusion parameters, such as singular value decomposition (SVD) and its variations, are known to be sensitive to noise and inaccuracies in the arterial input function. To our knowledge, there has been no implementation of deep learning methods for CT perfusion parameter estimation. Materials & methods: In this work, we propose a deep learning method based on the Transformer model, named CTPerformer-Net, for CT perfusion parameter estimation. In addition, our method incorporates some physical priors. We integrate physical consistency prior, smoothness prior and the physical model prior through the design of the loss function. We also generate a simulation dataset based on physical model prior for training the network model. Results: In the simulation dataset, CTPerformer-Net exhibits a 23.4 % increase in correlation coefficients, a 95.2 % decrease in system error, and a 90.7 % reduction in random error when contrasted with block-circulant SVD. CTPerformer-Net successfully identifies hypoperfused and infarcted lesions in 103 real CTP images from the ISLES 2018 challenge dataset. It achieves a mean dice score of 0.36 for the infarct core segmentation, which is slightly higher than the commercially available software (dice coefficient: 0.34) used as a reference level by the challenge. Conclusion: Experimental results on the simulation dataset demonstrate that CTPerformer-Net achieves better performance compared to block-circulant SVD. The real-world patient dataset confirms the validity of CTPerformer-Net. © 2024 Elsevier Ltd

关键词： Image segmentation

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data mining accelerated the design strategy of high-entropy alloys with the largest hardness based on genetic algorithm optimization

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Materials Genome engineering Advances 2024年第2期2卷 108-125页

作者： Xianzhe Jin Hong Luo Xuefei Wang Hongxu Cheng Chunhui Fan Xiaogang Li Xiongbo Yan Key Laboratory for Corrosion and Protection of The Ministry of Education(MOE) Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijingChina National Materials Corrosion and Protection Data Center Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijingChina Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijingChina

This article proposed a design strategy that integrated machine learning models based on random forest and genetic algorithm(GA)for the rapid screening of hardness in the AlCoCrCuFeMoNiTi high-entropy alloys *** feature engineering and modeling,valence electron concentration,atomic size difference(δr),Pauling electronegativity difference(Δχ),geometric parameters(Λ),and the Cr content were identified as the five key features in the *** GAwas employed to search for alloys with superior hardness and guided *** three itera-tions,the HEA Al_(18)Co_(21)Cr_(23)Fe_(23)Mo_(15)exhibiting the highest predicted hardness(868.8 HV)was *** alloy was predominantly composed of BCC,ordered B2,andσphases,with an experimental hardness of 899.8±9.9 HV,which as approximately 5.38%greater than the maximum hardness observed in the original *** design strategy can also solve other regression problems and pave the way for optimizing material performance in various engineering applications.

关键词： AlCoCrCuFeMoNiTi genetic algorithm hardness high-entropy alloys machine learning

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Models of Exchanged datasets and Interactions of Buyers in the data Market: Toward Multi-Agent Simulators for System Design 26

Models of Exchanged Datasets and Interactions of Buyers in t...

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26th International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Hayashi, Teruaki Matsushima, Hiroyasu Sakaji, Hiroki Fukami, Yoshiaki Shimizu, Takumi Department of Systems Innovation School of Engineering The University of Tokyo Tokyo Japan Center for Data Science Education and Research Shiga University Shiga Japan Department of International Digital and Design Management Tokyo University of Science Tokyo Japan Faculty of Policy Management Keio University Kanagawa Japan

Value creation by reusing data as exchangeable resources has been widely studied as a new source of innovation, resulting in the establishment of a data market and its ecosystem. However, owing to the nascent nature of the market, observable information on exchanged datasets and interactions among stakeholders, such as buyers and providers, in the market are limited. Therefore, a lack of observable information that contributes to designing the market system and formulating regulations to promote the sound growth of data markets exists. This study modeled exchanged datasets and data buyers as agents, the smallest units of the data market components, and prepared seven scenarios for four market sizes. We simulated the effects of datasets and agent models with different market sizes on the agents' data purchases and emergence of popular datasets and discussed the factors that affect the purchase frequency distribution of the datasets. We present the experimental results and new implications for developing a data market simulator. The development of this simulator is expected to significantly advance research on the market understanding and system design in data markets. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & engineering Systems (KES 2022)

关键词： Systems analysis

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Pre-training with asynchronous supervised learning for reinforcement learning based autonomous driving

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Frontiers of Information Technology & Electronic engineering 2021年第5期22卷 673-686页

作者： Yunpeng WANG Kunxian ZHENG Daxin TIAN Xuting DUAN Jianshan ZHOU Beijing Advanced.Innovation Center for Big Data and Brain Computing School of Transportation Science and EngineeringBeihang UniversityBeijing 100191China

Rule-based autonomous driving systems may suffer from increased complexity with large-scale intercoupled rules,so many researchers are exploring learning-based *** learning(RL)has been applied in designing autonomous driving systems because of its outstanding performance on a wide variety of sequential control ***,poor initial performance is a major challenge to the practical implementation of an RL-based autonomous driving *** training requires extensive training data before the model achieves reasonable performance,making an RL-based model inapplicable in a real-world setting,particularly when data are *** propose an asynchronous supervised learning(ASL)method for the RL-based end-to-end autonomous driving model to address the problem of poor initial performance before training this RL-based model in real-world ***,prior knowledge is introduced in the ASL pre-training stage by asynchronously executing multiple supervised learning processes in parallel,on multiple driving demonstration data *** pre-training,the model is deployed on a real vehicle to be further trained by RL to adapt to the real environment and continuously break the performance *** presented pre-training method is evaluated on the race car simulator,TORCS(The Open Racing Car Simulator),to verify that it can be sufficiently reliable in improving the initial performance and convergence speed of an end-to-end autonomous driving model in the RL training *** addition,a real-vehicle verification system is built to verify the feasibility of the proposed pre-training method in a real-vehicle *** results show that using some demonstrations during a supervised pre-training stage allows significant improvements in initial performance and convergence speed in the RL training stage.

关键词： Self-driving Autonomous vehicles Reinforcement learning Supervised learning

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Application of Bayes’ Theorem in an Expert System for Diagnosing Mangosteen Diseases and Pests 22

Application of Bayes’ Theorem in an Expert System for Diagn...

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Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications

作者： Foni Agus Setiawan Freza Riana Siti Aisyah Research Center for Data and Information Science National Research and Innovation Agency Indonesia and Department of Informatics Engineering Universitas Ibn Khaldun Indonesia Department of Informatics Engineering Universitas Ibn Khaldun Indonesia

ISBN: (纸本)9781450397902

Mangosteen is one of the typical fruits of Southeast Asia and Indonesia's flagship fruit with promising export prospects. However, the poor quality of the mangosteen fruit, which is caused by diseases and pest attacks, is an obstacle in increasing export targets. Disease and pest attacks can be immediately overcome if farmers can identify the types of pests and diseases quickly based on the symptoms that appear on the mangosteen plant. The lack of knowledge of farmers in identifying the symptoms that appear and the limited number of experts lead to the late process of handling pests and diseases of the mangosteen plant. Therefore, this study aimed to build an expert system for diagnosing mangosteen diseases and pests using Bayes’ theorem. The knowledge base contained data of 5 diseases, 5 pests, and 42 symptoms. The inference engine utilized the Bayes’ theorem to produce a probability value of a disease or pest attacking the mangosteen plant based on the symptoms that arise. This study yielded an accuracy value of 94% based on 50 test data. This expert system can help farmers in identifying the diseases and pests that attack mangosteen plants quickly as well as finding solutions to the problem.

关键词： mangosteen diseases expert system plant pests Bayes’ theorem

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