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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： He, Jingfei Mao, Yongfei Zhao, Liangbo Cui, Zihan Wang, Yu Shi, Hao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China China Academy of Space Technology Beijing Institute of Space Mechanics and Electricity Beijing China

ISBN: (纸本)9798331515669

Synthetic Aperture Radar (SAR) images own dominant merits, such as all-weather and all-day working conditions, but it is difficult for unprofessional people to interpret SAR images. Translating SAR images into optical images to assist interpretation can facilitate the transformation of SAR data into usable information. Therefore, an advanced SAR to Optical (S2O) image translation method utilizing a parallel Generative Adversarial Network (GAN) framework was proposed. This method incorporates an optical image reconstruction network alongside the S2O translation network, enhancing the fidelity and color consistency of the translated images. By employing a domain alignment module and a deep supervision feature loss module, the network effectively utilizes abundant optical image features to overcome the scarcity of SAR-optical image pairs. Experiments conducted on the SEN1-2 dataset demonstrate superior performance over existing methods, particularly in preserving detailed structural information in images. © 2024 IEEE.

关键词： Generative adversarial networks

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Improving the Robustness of Low-Resource Neural Machine Translation with Adversarial Examples 1

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18th China Conference on Machine Translation, CCMT 2022

作者： Sun, Shuo Hou, Hongxu Wu, Nier Yang, Zongheng Wang, Yisong Wang, Pengcong Jian, Weichen Inner Mongolia Key Laboratory of Mongolian Information Processing Technology National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9789811979606

ISBN: (纸本)9789811979590

Weak robustness and noise adaptability are major issues for Low-Resource Neural Machine Translation (NMT) models. That is, once some tiny perturbs are added to the input sentence, the model will produce completely different translation with high confidence. Adversarial example is currently a major tool to improve model robustness and how to generate an adversarial examples that can degrade the performance of the model and ensure semantic consistency is a challenging task. In this paper, we adopt reinforcement learning to generate adversarial example for low-resource NMT. Specifically, utilizing the actor-critic algorithm to modify the source sentence, the discriminator and translation model in the environment are used to determine whether the generated adversarial examples maintain semantic consistency and the overall deterioration of the model. Furthermore, we also install a language model reward to measure the fluency of adversarial examples. Experimental results on low-resource translation tasks show that our method highly aggressive to the model while maintaining semantic constraints greatly. Moreover, the model performance is significantly improved after fine-tuning with adversarial examples. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural machine translation

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Mongolian Medicine Named Entity Recognition via Dictionary-Based Synonym Generalization 9

Mongolian Medicine Named Entity Recognition via Dictionary-B...

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9th IEEE International Conference on Cloud Computing and Intelligence systems, CCIS 2023

作者： Qin, Si Bao, Feilong Dulamragchaa, Uuganbaatar Natl. and Loc. Jt. Eng. Res. Center of Intelligent Information Processing Technology for Mongolian Huhhot Inner Mongolia010021 China Natl. and Loc. Jt. Eng. Res. Center of Intelligent Information Processing Technology for Mongolian Huhhot Inner Mongolia010021 China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Huhhot Inner Mongolia010021 China Institute of Mathematics and Digital Technology Mongolian Academy of Sciences Ulaanbaatar999097 Mongolia

ISBN: (纸本)9798350304428

The task of named entity recognition in Mongolian medicine poses challenges due to the diversity and complexity of terminology and herbal names. Variations in names used across different literature sources and regions hinder accurate recognition, as traditional dictionary-based methods often fail to encompass all synonyms and variants. To address this issue, this paper presents a lexicon-based synonym generalization method. By leveraging lexical resources in Mongolian, this approach extends the synonyms of common herbs and terms, incorporating additional synonyms and related words into the named entity recognition system. Consequently, the system becomes capable of recognizing a wider range of names and variants. In this study, the authors construct the Mongolian Medicine Named Entity Recognition Datasets (MMNER), comprising five entity types and 44,968 entities. The datasets are developed using the proposed synonym generalization framework. Subsequently, a series of deep learning models are employed to conduct experimental comparisons on the MMNER datasets. The experimental results demonstrate that by enhancing synonym coverage, the recognition of Mongolian medicine entities in text achieves a significantly improved accuracy, with an F1 value of 93.66%. These findings signify a noteworthy enhancement in the performance of Mongolian medicine named entity recognition. © 2023 IEEE.

关键词： Deep learning

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User Behavior-Based Collaborative Filtering Recommendation Algorithm and Visualization in Second-Hand Trading Platform 2

User Behavior-Based Collaborative Filtering Recommendation A...

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2nd IEEE International Conference on Sensors, Electronics and Computer Engineering, ICSECE 2024

作者： Qiao, Rui Wang, Xin Zhong, Xiaoben School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan China Yunnan College of Business Management Anning650301 China

ISBN: (纸本)9798350373646

In the backdrop of the digital era, the demand for second-hand trading within campuses has seen a significant rise. This project aims at designing a second-hand trading platform that merges the latest technological trends, providing a secure and convenient trading environment for students. The system utilizes Spring Boot for backend development and Vue along with ElementUI for constructing the user interface, ensuring efficient operation and enhancing user interaction experience. The server employs Spring Boot's built-in Tomcat, simplifying the deployment process. For data persistence, Mybatis framework is used for flexible and efficient interactions with the MySQL database. The platform introduces a collaborative filtering algorithm based on user behavior for smart product recommendations, further improving the user experience. The system integrates core modules such as user management, product browsing, shopping cart functionalities, and order processing, innovatively introducing an offline transaction confirmation mechanism to ensure transaction security and reliability while avoiding legal risks associated with online payments. With its B/S architecture, the platform operates more smoothly, making it more accessible to users. Experimental results demonstrate that the platform has a user-friendly interface and efficient data processing capabilities, receiving widespread recognition and praise from users, proving its practicality and innovativeness. © 2024 IEEE.

关键词： Collaborative filtering

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Saliency-SLAM: Saliency Prediction for Visual SLAM

Saliency-SLAM: Saliency Prediction for Visual SLAM

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2023 Tianjin University-IET Electrical and information Engineering Doctoral Forum, TJU IET 2023

作者： Jin, Sheng Meng, Qinghao Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781839539824

This paper proposes a novel approach for visual simultaneous localization and mapping (vSLAM), called Saliency-SLAM. This method effectively emulates the visual attention mechanism by integrating a saliency prediction model. In contrast to conventional vSLAM methods, which treat all feature points as equally significant, salient feature points are given greater importance in Saliency-SLAM. Therefore, we construct a novel saliency dataset specifically for vSLAM task to train a saliency prediction model by integrating multi-level visual information. Then, we propose to use the salient value of predicted result as the weight of significance for the feature points during the optimization process. Exhaustive experiments have been conducted on the KITTI dataset, demonstrating that our proposed method outperforms existing approaches in terms of localization accuracy. © The Institution of Engineering & technology 2023.

关键词： Forecasting

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A Sensitive Nanothermometer Based on DNA Triplex Structure 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Chen, Zhekun Hu, Yingxin Xie, Chun Chen, Kuiting Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang050043 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

DNA has been used to construct a variety of nanoscale thermometers due to its unique thermodynamic properties. It has proven to be one of the most promising, yet challenging aspects of DNA-based thermometers, to obtain significant signal changes when the temperature changes slightly. Here, we propose a strategy to construct nanoscale thermometers with sensitive temperature responses based on DNA triplex. The thermometers consist of a CT-rich DNA strand and stabilizing strands, whose conformations vary with temperature. By adjusting the sequence design or introducing mismatch bases in stabilizing strands, the temperature response interval of the thermometer can be reduced to 7 ∘C, which lead to higher sensitivity compared to the interval of 12–15 ∘C for conventional designs. The temperature responses of the thermometer are characterized by fluorescence experiments. The fluorescence kinetics experiments demonstrate the good repeatability. The design of the nanothermometer will be helpful for constructing advanced nanosystem with sensitive temperature responses as well as long service times. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Thermometers

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A prompt-based approach to adversarial example generation and robustness enhancement

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Frontiers of Computer Science 2024年第4期18卷 85-96页

作者： Yuting YANG Pei HUANG Juan CAO Jintao LI Yun LIN Feifei MA Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Computer Science Stanford UniversityCA 94305USA School of Computing National University of SingaporeSingapore 119077Singapore Laboratory of Parallel Software and Computational Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Recent years have seen the wide application of natural language processing(NLP)models in crucial areas such as finance,medical treatment,and news media,raising concerns about the model robustness and *** find that prompt paradigm can probe special robust defects of pre-trained language *** prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via *** results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym ***,we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization *** on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks.

关键词： robustness adversarial example prompt learning pre-trained language model

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Corrigendum to "Hyperledger sawtooth based supplychain traceability system for counterfeit drugs" [Comput. Ind. Eng. 190 (2024) 110021] (Computers & Industrial Engineering (2024) 190, (S0360835224001426), (10.1016/***.2024.110021))

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Computers and Industrial Engineering 2025年 204卷

作者： Nawaz, Anum Wang, Liguan Irfan, Muhammad Westerlund, Tomi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Turku Intelligent and Embedded Robotic Systems Lab Faculty of Technology University of Turku Finland

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A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

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Differentiator-Based Adaptive H8 Tracking Control of Fully Actuated systems 3

Differentiator-Based Adaptive H8 Tracking Control of Fully A...

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3rd Conference on Fully Actuated System Theory and Applications, FASTA 2024

作者： Feng, Yuxin Li, Zhiqiang Liu, Yang He, Zhaoshui Li, Hongyi Guangdong University of Technology School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Qingdao University of Science and Technology School of Automation and Electronics Engineering Qingdao266061 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China

ISBN: (纸本)9798350373691

This article addresses an adaptive tracking control problem for uncertain high-order fully actuated (HOFA) systems with unknown parameters and disturbances. Under the framework of backstepping, the unknown parameter is estimated and the external disturbance is handled by using adaptive control and H8 technique, respectively. Moreover, the Levant differentiator is employed to reduce the computation burden. Additionally, the boundedness of signals in the closed-loop system is proven using the Lyapunov theory. Finally, the effectiveness of the proposed scheme is validated through the simulation study. © 2024 IEEE.

关键词： Backstepping

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