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Robot Debater: Debate-styled Text Auto-generation System Based on Large Foundation Language Models

Robot Debater: Debate-styled Text Auto-generation System Bas...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Yu Zhu Yijun Ling Xufeng Ling Jie Yang Shanghai Library (Institute of Scientific and Technical Information of Shanghai) Shanghai China Shanghai Xiangming High School Shanghai China School of Artificial Intelligence Shanghai Normal University Tianhua College Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

We use a large foundation language model, which is fine-tuned with debate corpora, to develop a robot debater application. To address the limitations of requiring immense computational power in large base language models, this study takes advantage of the Low Rank Adaption characteristic prevalent in domain expert knowledge. By applying Low Rank Adaption technology and fine-tuning with a dedicated dataset, the computational load is reduced to just one-thousandth of what is needed for a large language model, greatly expanding the application scenarios of robot debaters using large language models. In view of the characteristics of debate competitions, this model can preset a variety of debate scenarios and supports personalized debate processes. It employs intelligent voice recognition technology combined with a multi-channel voice input method, allowing for precise localization of different human debaters and improving the accuracy of voice input recognition. the system can support multiple large-scale language generation models and utilize various different voice broadcasting systems, including male and female voice styles, as well as a range of voice emotions. this model can be applied to debate competitions held in universities, high schools, and various industries. It can support human-machine debates as well as machine-to-machine debates.

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Identification of Alzheimer’s Disease Associated Genes through Explicable Deep learning and Bioinformatic

Identification of Alzheimer’s Disease Associated Genes thro...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Xiangrong Sun Wei Shu Yuxin Zhang Xiansheng Huang Juxia Liu Yuan Liu Tiejun Yang Department of intelligent medicine College of intelligent medicine and biotechnology Key Laboratory of medical biotechnology and translational medicine of Guangxi education department Guilin Medical University Guilin China Department of intelligent medicine College of intelligent medicine and biotechnology Guilin Medical University Guilin China

We utilized interpretable deep learning to identify key genes and potential biomarkers associated with Alzheimer’s disease (AD). AD-related gene expression data from GEO datasets and Alzdata were collected. To create a training set, we performed differential expression analysis to delete low-expressed genes. To explore disease-related genes and potential biomarkers, we employed a pathway-centric deep learning network, which integrated gene expression data with established biological pathways, building upon the foundation of PASNet. Our study employed a pathway-related deep learning model based on PASNet combined with bioinformatic analysis for AD risk prediction, and achieved a good performance (AUC = 0.82, F1 score = 0.73). the model accurately classified AD patients based on gene expression data from the brain tissue while providing interpretability deep neural networks. Potential biomarkers (DYNC1I1, DNAJC1, SCRIB, TFEB, MAPKAPK3) and biological pathways (Senescence-Associated Secretory Phenotype, Transcriptional activity of SMAD2/SMAD3:SMAD4 heterotrimer, Toll-like Receptor Cascades) associated with AD were identified. We have employed a combination of bioinformatics analysis and deep learning models to identify a series of candidate genes associated with the diagnosis of Alzheimer’s disease (AD). Moreover, we have validated their potential as biomarkers through the utilization of external datasets, and finally focus on the five genes DYNC1I1, DNAJC1, SCRIB, TFEB, and MAPKAPK3.

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Shearer cutting pattern recognition based on multi-scale fuzzy entropy and support vector machine 4

Shearer cutting pattern recognition based on multi-scale fuz...

引用

2020 4th international conference on Energy Material, Chemical Engineering and mining Engineering, EMCEME 2020

作者： Liang, Bin Liu, Ze Niu, Yanbo China University of Mining and Technology Xuhai College Xuzhou221008 China China University of Mining and Technology School of Mechatronic Engineering Xuzhou221116 China

Aiming at the problem of low intelligent level of shearer, a shearer cutting pattern recognition method is proposed based on the combination of multi-scale fuzzy entropy, Laplace score and support vector machine. By extracting the multi-scale fuzzy entropy of the vibration signal under different cutting modes, the feature vector representing the cutting pattern is mastered. At the same time, the Laplace score is used to select the feature vectors with possessing rich cutting pattern information. the selected features are produced as the learning samples of support vector machine. the experimental system of shearer cutting coal-rock is built, and the vibration signals of rocker arm under different cutting patterns are extracted. the experimental analysis is carried out and the results indicate that the cutting pattern recognition method proposed in this paper has high recognition accuracy, and the correct rate can reach to 98.86%. the research results provide technical support for the intelligent and rapid development of fully mechanized mining face. © Published under licence by IOP Publishing Ltd.

关键词： Support vector machines

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Research on malicious traffic detection methods based on deep learning 4

Research on malicious traffic detection methods based on dee...

引用

4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： Tang, Bin Hui Deng, Shang Yu Wu, Chun Peng Wang, Ze Feng An, Jian Ping Zhao, Zi Fa Jincheng College of Sichuan University School of ComputerSoftware Sichuan University School of Cyber Science and Engineering Sichuan Chengdu China Jincheng College of Sichuan University School of ComputerSoftware Sichuan Chengdu China

ISBN: (纸本)9781665413220

the number of malicious traffic in the real network is growing rapidly, such as common DoS attacks, web attacks, DDoS attacks, and so on. these attacks may cause huge economic losses to enterprises, countries, and individuals. Traditional machine learning methods are hardly meeting the accuracy and real-time requirements of malicious traffic identification technology in the face of huge and multi-dimensional online data. Large-scale data can provides training data for deep learning, and deep learning can directly obtain advanced features from the data. therefore, the malicious traffic identification technology based on a convolutional neural network (CNN) is adopted. the outliers and useless features in the data set are removed by preprocessing, and then the model is obtained by multiple training and optimization of the convolutional neural network. the experimental results show that CNN performs well in the malicious traffic detection tasks, and the metric F1 values reach 95%, which is better than the deep learning models such as RNN, and CRNN. © 2021 IEEE.

关键词： Cybersecurity

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Deep learning-based vehicle tracking and traffic event detection

Deep learning-based vehicle tracking and traffic event detec...

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Computer Vision, Image and Deep learning (CVIDL), international conference on

作者： Guohao Sun Kehuan Yan Chengbin Fan School of Mathematics Tianjin University Tianjing China School of Computer and Data Science Fuzhou University Fujian China School of Electrical Engineering and Automation Xiamen University Tan Kah Kee College Fujian China

Time-series data mining plays an important role in big data decision making because it can reveal the development pattern of things. Similar concatenation of temporal data is a fundamental prerequisite for data twinning., whose core objective is to find all similar temporal data pairs according to a given similarity metric. Dynamic temporal regularization (DTW) has been widely used in many fields., such as target detection., trend prediction and fault identification., as the best data alignment method on temporal data. We use deep learning to extract vehicle trajectories and perform behavioral pattern learning., and finally use an improved DTW algorithm to pre-process the trajectory data and solve the distance function to achieve matching between the trajectories of the event sequence to be measured and the typical trajectory data patterns. By comparing the indicators with the unimproved DTW algorithm., the research results show that this traffic condition recognition method is stable and reliable., and can maintain high matching accuracy with significantly reduced computation., high success rate and good real-time performance.

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Research and implementation of automatic identification of signal modulation regime based on artificial neural network 24

Research and implementation of automatic identification of s...

引用

4th international conference on Big data, Artificial Intelligence and Risk Management, ICBAR 2024

作者： Gu, Rui Cong, Bo Zhou, Chengbin Wu, Youxing Liu, Sifang China Satellite Maritime Measurement and Control Department Jiangsu Jiangyin China

ISBN: (纸本)9798400709753

the identification of modulation regime of telemetry, remote sensing and communication signals is an important part of signal detection. In this paper, we extract the instantaneous features of signals through Hilbert transform, and construct eight feature parameters on this basis, and use artificial neural network classifier to realize the automatic identification of signal modulation regime. the experimental results show that the artificial neural network classifier has one higher recognition accuracy than the traditional method based on decision classification tree, which is especially suitable for offline analysis of signals after the fact. © 2024 Copyright held by the owner/author(s).

关键词： artificial neural networks automatic modulation recognition digital modulation machine learning

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Crops Analysis And Classification Using machine learning Techniques Based on Soil and Environmental Characteristics

Crops Analysis And Classification Using Machine Learning Tec...

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Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： Bharath M Gowtham S Trupthi Rao Ashwini Kodipalli Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bangalore Karnataka India

In the evolving landscape of modern agriculture, the fusion of technology with traditional methods is essential. this study explores the transformative potential of machine learning in crop classification, leveraging soil and environmental data. the K-Nearest Neighbors (KNN) model achieved an impressive 97.5% accuracy in pattern recognition, while ensemble models, such as Random Forest, Naïve Bayes, and XGBoost, excelled with a remarkable 99% accuracy, surpassing conventional methods. this research underscores the profound impact of machine learning in advancing agriculture, facilitating precision farming, and addressing global concerns like food security and sustainability. Additionally, the practical implications of this research are significant for farmers, providing them with the means to select crops based on their environmental characteristics, optimizing resource allocation and crop management, and contributing to agricultural sustainability in an era of environmental unpredictability and climate change.

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Library User Behavior and Service Optimization Using Artificial Intelligence

Library User Behavior and Service Optimization Using Artific...

引用

Electronic Communications, Internet of things and Big data (ICEIB), international conference on

作者： Ping Wang Shandong Institute of Commerce and Technology Jinan China

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Library user behavior was investigated using artificial intelligence (AI) technology to propose corresponding service optimization strategies. through data collection and analysis, the behavioral characteristics of users' borrowing, querying, and reading were determined. machine learning and data mining were used to identify and predict the patterns of user behavior and provide personalized recommendations and services. the effectiveness and feasibility of the proposed strategies were verified in case studies and field research.

关键词： data privacy data security Quality of service machine learning Big data Libraries Internet of things

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Research on Intelligent Sensitive data recognition and Annotation Technology Based on Long Text

Research on Intelligent Sensitive Data Recognition and Annot...

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Electronics, Circuits and Information Engineering (ECIE), international conference on

作者： Hua Fu Liang Meng Sisi Wei Chunkai Nong Yu Tan Guangxi Power Grid Co. Ltd. Nanning Power Supply Bureau Nanning China Information Communication Branch of Guangxi Power Grid Company Nanning China

ISBN: (数字)9798350388312

ISBN: (纸本)9798350388329

With the rapid development of information technology, the widespread dissemination of long text data in cyberspace has brought challenges to personal privacy and information security. this study aims to develop an intelligent sensitive data recognition and annotation technology for long text content. By integrating natural language processing (NLP) technology with machine learning algorithms, we propose an efficient recognition framework that can accurately extract and annotate sensitive information from long texts. the research results indicate that the framework performs well in processing long text data, effectively improving the accuracy of sensitive data recognition, and providing solid technical support for privacy protection and data security.

关键词： data privacy machine learning algorithms Text recognition Annotations data security Information security Solids

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Research on LncRNA and Disease Associations Prediction Base on data mining 4

Research on LncRNA and Disease Associations Prediction Base ...

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2021 4th international conference on Modeling, Simulation and Optimization Technologies and Applications, MSOTA 2021

作者： Jia, Fengjiao Jiang, Shaohua Wu, Zaoyang Liang, Yigao College of Information Science and Engineering Hunan Normal University Changsha China

the accumulated experiments show that lncRNA has a role in biophysiological and case processes. Prediction of the relationship between diseases and lncRNA will contribute to clarify the etiology of diseases, develop new drugs and treat complex diseases. However, the traditional biological experiment method has long experiment period and high cost. therefore, based on existing biological data and biological experimental data, data mining techniques have been used to propose many models and methods to predict the lncRNA-disease correlation. this article will provide a specific introduction to lncRNA and disease-related databases, which summarizes some relevant predictions classical models (matrix factorization, heterogeneous networks, machine learning). At the end of the article, the problems of lncRNA and the current prediction of the disease are analyzed, and some ideas and help are provided for later researchers. © Published under licence by IOP Publishing Ltd.

关键词： data mining

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