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15th International Congress on Image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2022

作者： Dong, Zhaoxu Chen, Zhao Wang, Qian School of Computer Science and Technology Donghua University China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Retrosynthetic analysis is one of the most basic and commonly used methods for compound synthesis routes planning. In the process, the single-step synthesis prediction is the basis for predicting the synthesis route of the whole compound. With the wide application of computers in various disciplines, the use of computer-aided retrosynthetic process is becoming more and more common. The rise of artificial intelligence also makes more and more people apply pure data-driven deep learning models to retrosynthetic methods. At present, there are many deep learning-based methods to solve the problem of single-step retrosynthetic prediction. However, there is a lack of an end-to-end method using graph convolutional neural network for prediction. In this paper, we propose a template-based graph relation network for the prediction of single-step synthesis of compounds. The model can learn the coding of molecules and templates to predict whether there is a relationship between them. Therefore, the reactants of target molecules predicted by this model have great interpretability. In addition, in this experiment, we used a new dataset, which has a variety of reaction and template data, and further verified the practicability of the model. © 2022 IEEE.

关键词： Forecasting

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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A data complementary method for thunderstorm point charge localization based on atmospheric electric field apparatus array group

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Digital Communications and Networks 2021年第2期7卷 170-177页

作者： Xu Yang Hongyan Xing Collaborative Innovation Center for Meteorological Disaster Prediction and Evaluation Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Key Laboratory of Meteorological Detection and Information Processing Nanjing University of Information Science and TechnologyNanjing210044China

Data loss or distortion causes adverse effects on the accuracy and stability of the thunderstorm point charge *** solve this problem,we propose a data complementary method based on the atmospheric electric field apparatus array *** electric field component measurement model of the atmospheric electric field apparatus is established,and the orientation parameters of the thunderstorm point charge are *** on the mirror method,the thunderstorm point charge coordinates are obtained by using the potential distribution *** test the validity of the basic algorithm,the electric field component measurement error and the localization accuracy are *** the azimuth angle and the elevation angle,the localization parameters also include the distance from the apparatus to the thunderstorm *** on a primary electric field apparatus,we establish the array group of *** on this,the data measured by each apparatus is complementarily processed to regain the thunderstorm point charge *** results show that,compared with the radar map data,this method can accurately reflect the location of the thunderstorm point charge,and has a better localization ***,several observation results during thunderstorm weather have been presented.

关键词： Atmospheric electric field apparatus Array Thunderstorm point charge Data complementary

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An efficient split learning framework for recurrent neural network in mobile edge environment 22

An efficient split learning framework for recurrent neural n...

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2022 Conference on Research in Adaptive and Convergent Systems, RACS 2022

作者： Zhou, Wenxuan Qu, Zhihao Zhao, Yanchao Tang, Bin Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources and School of Computer and Information Hohai University Jiangsu Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China

ISBN: (纸本)9781450393980

Split learning is an emerging distributed machine learning paradigm that exhibits great advantages in privacy protection and training efficiency. Most previous studies pay attention to client-server based split learning on Convolutional Neural networks (CNNs), and little effort has been spent on investigating the Recurrent Neural Networks (RNNs) oriented split learning. To fill in this gap, we propose a multi-hop split learning algorithm for RNN in mobile edge computing, namely M-SLRNN. The core idea is to efficiently orchestrate resource-constrained mobile devices to collaboratively train a large-size model. We formulate the optimization problem with the objective of minimizing the total training time and theoretically analyze its hardness. Then, we propose an efficient algorithm that conditionally has an approximation ratio of 2. Extensive experiments on both image and text classification tasks demonstrate the effectiveness and the efficiency of the proposed algorithm. © 2022 ACM.

关键词： Recurrent neural networks

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Offline Reinforcement Learning with Policy Guidance and Uncertainty Estimation

Offline Reinforcement Learning with Policy Guidance and Unce...

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

作者： Lan Wu Quan Liu Lihua Zhang Zhigang Huang School of Computer Science and Technology Soochow University Soochow China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China

Offline reinforcement learning is an approach for transforming static datasets into powerful decision engines. It cannot interact with the environment online, which leads to distribution shifts. Previous approaches addressed this problem by making the current policy as close as possible to the behavior policy. However, this type of approach severely limits the generalization ability of Q-functions. To address the above concerns, offline reinforcement learning with policy guidance and uncertainty estimation (PGUE) is proposed. PGUE proposes a fine-grained adjustment approach that improves the generalization ability of Q-functions using a perturbation model. The enhancement of the out-of-distribution generalization of Q-functions is achieved through the implicit guidance of the state space via a deterministic latent policy. Meanwhile, integrating uncertainty estimation into the loss function improves the in-distribution generalization of Q-functions. On the D4RL benchmark, PGUE has better performance than baselines. Moreover, we verify the state distribution and its out-of-distribution generalization ability.

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An Ensemble Learning Approach for Wind Power Forecasting 3

An Ensemble Learning Approach for Wind Power Forecasting

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3rd International Conference on Electronic information Engineering and computer, EIECT 2023

作者： Huang, Yuesheng Chen, Sida Wu, Qiling Xiao, Jiapeng Ren, Yongqi Wang, Leijun Chen, Rongjun Li, Jiawen Guangdong Polytechnic Normal University School of Computer Science Guangzhou China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350357707

With the growing global energy demand and requirement for environmental protection, renewable energy is attracting attention as a vital development direction. Particularly, wind power is rapidly developing as a clean and sustainable form of energy. Nonetheless, wind speed uncertainty and system dynamics make wind power prediction and optimization complex and challenging. Accurate wind power prediction is essential to improve system reliability and performance. In this regard, traditional forecasting methods have limitations in some cases. In this study, based on XGBoost and decision tree with Bagging methods in ensemble learning, wind power data is predicted. The results indicated that better performances than the traditional linear regression and ordinary decision tree models are achieved, revealing the efficiency of the proposed approach. © 2023 IEEE.

关键词： Decision trees

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Effect of non-condensable gas on a collapsing cavitation bubble near solid wall investigated by multicomponent thermal MRT-LBM

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Chinese Physics B 2021年第2期30卷 367-376页

作者： Yu Yang Ming-Lei Shan Qing-Bang Han Xue-Fen Kan Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology Hohai UniversityChangzhou 213022China College of Computer and Information Hohai UniversityNanjing 210000China

A multicomponent thermal multi-relaxation-time(MRT)lattice Boltzmann method(LBM)is presented to study collapsing cavitation *** simulation results satisfy Laplace law and the adiabatic law,and are consistent with the numerical solution of the Rayleigh-Plesset *** study the effects of the non-condensable gas inside bubble on collapsing cavitation bubble,a numerical model of single spherical bubble near a solid wall is *** temperature and pressure evolution of the two-component two-phase flow are well *** addition,the collapse process of the cavitation bubble is discussed elaborately by setting the volume fractions of the gas and vapor to be the only *** results show that the non-condensable gas in the bubble significantly affects the pressure field,temperature field evolution,collapse velocity,and profile of the *** distinction of the pressure and temperature on the wall after the second collapse becomes more obvious as the non-condensable gas concentration increases.

关键词： multicomponent cavitation bubble non-condensable gas lattice Boltzmann method

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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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A Straw Coverage Rate Calculation Method Based on Single Image processing 2

A Straw Coverage Rate Calculation Method Based on Single Ima...

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2nd International Conference on Artificial Intelligence, Human-computer Interaction and Robotics, AIHCIR 2023

作者： Wu, Shiqi Guo, Xi Li, Jie Wulamu, Aziguli Li, Tian Zou, Qiping Xia, Chenglong School of Computer and Communication Engineering University of Science and Technology Beijing Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Beijing Agricultural Machinery Manager IoT Technology Co. Ltd Beijing China Hechi University Key Laboratory of Ai and Information Processing Hechi China

ISBN: (纸本)9798350360363

This paper proposes a straw coverage rate calculation method based on single-image processing. This method uses a convolutional neural network (CNN) to extract features from images of fields covered with straw, classifies the image based on the features, and then uses an image binarization method to distinguish the straw and the land parts in the image and calculate the straw coverage rate based on the proportion of the straw part. This method has been applied in the land classification project in a particular region. These results demonstrate that this method effectively calculates straw coverage rates and can be used in other fields of precision agriculture. © 2023 IEEE.

关键词： Image recognition

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Secure beamforming designs for maximizing secrecy sum rate in MISO-NOMA networks

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Digital Communications and Networks 2025年第1期11卷 83-91页

作者： Yanbo Zhang Zheng Yang Jingjing Cui Xianfu Lei Yi Wu Jun Zhang Chao Fang Zhiguo Ding College of Photonic and Electronic Engineering Fujian Normal UniversityFuzhou 350007China School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Institute of Mobile Communications Southwest Jiaotong UniversityChengdu 610031China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China Faculty of Information Technology Beijing University of TechnologyBeijingChina School of Electrical and Electronics Engineering The University of ManchesterManchester M139PLUK

In this paper,the application of Non-Orthogonal Multiple Access(NOMA)is investigated in a multiple-input single-output network consisting of multiple legitimate users and a potential *** support secure transmissions from legitimate users,two NOMA Secrecy Sum Rate Transmit Beam Forming(NOMA-SSR-TBF)schemes are proposed to maximise the SSR of a Base Station(BS)with sufficient and insufficient transmit *** BS with sufficient transmit power,an artificial jamming beamforming design scheme is proposed to disrupt the potential eavesdropping without impacting the legitimate *** addition,for BS with insufficient transmit power,a modified successive interference cancellation decoding sequence is used to reduce the impact of artificial jamming on legitimate *** specifically,iterative algorithm for the successive convex approximation are provided to jointly optimise the vectors of transmit beamforming and artificial *** results demonstrate that the proposed NOMA-SSR-TBF schemes outperforms the existing works,such as the maximized artificial jamming power scheme,the maximized artificial jamming power scheme with artificial jamming beamforming design and maximized secrecy sum rate scheme without artificial jamming beamforming design.

关键词： NOMA Secure transmission Artificial jamming Beamforming design

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