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Code Recommendation Based on Deep Learning 12

Code Recommendation Based on Deep Learning

引用

12th international conference of Information and Communication Technology, ICTech 2023

作者： Sun, Hao Xu, ZhiFei Li, Xu Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China The Chinese University of Hongkong Shenzhen Shenzhen China Beijing Zhongke Zhihe Digital Technology Co. Ltd Beijing China

ISBN: (纸本)9798350332919

Software developers, especially programming beginners, often need to complete some unfamiliar coding tasks, generally by reusing the previous code or searching the existing code snippets, and from a large number of redundant search results to screen out the code with similar structure to complete the coding task. In order to quickly and accurately recommend high-quality code snippets for programmers, this paper Sentence-BERT model. Firstly, the method obtains a large amount of high-quality data to build the code base, and proposes a code snippet recommendation method based on generates sentence vectors with rich semantics for the natural language description corresponding to the code snippets and the natural language query input by users based on the deep learning model SBERT. then, the code snippets are recommended by comparing the dot product similarity. the effectiveness of the proposed method is verified by comparing the three commonly used evaluation indexes of hit rate, average reciprocal ranking and average accuracy with the methods in the current related research. Experimental results show that this recommendation method can effectively improve the accuracy and effect of code snippet recommendation. © 2023 IEEE.

关键词： Semantics

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Design and Implementation of an IoT-Based Smart Irrigation System for Optimized Water Management in Wheat Cultivation 12

Design and Implementation of an IoT-Based Smart Irrigation S...

引用

12th international conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Shukla, Ravi Shankar George, Remya P. Hussain, Sajithunisa Ahmad, Nazia Babu, Prakash Jain, Vishal College of Computing and Informatics Saudi Electronic University Department of Computer Science Saudi Arabia The Applied College Imam Abdulrahman Bin Faisal University Computer Science Department Saudi Arabia Acharya Institute of Technology Dept of It Bangalore India

ISBN: (纸本)9798350369861

the current study presents the planning and implementation of a custom-built, Internet of things (IoT)-based smart irrigation system for wheat cultivation that helps to combat problems such as water scarcity and inefficiency in traditional irrigation methods. the proposed device employs a network of environmental sensor that ensures optimal crop development with minimum water waste. these sensors monitor soil temperature and moisture levels and shows significant improvements in water management efficiency. When compared to standard irrigation approaches, the system's pilot installation results reveal considerable water usage reductions and benefits in the health of the wheat crop. these findings prove that the system is efficient at optimizing resource use and show how it can change the way wheat is farmed forever. © 2023 IEEE.

关键词： Internet of things

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Design Method of Component-Based Party Building Visualization Large-screen System 12

Design Method of Component-Based Party Building Visualizatio...

引用

12th international conference of Information and Communication Technology, ICTech 2023

作者： Qin, Wei Li, Jin Li, Xu Zhao, Shijie Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Beijing Zhongke Zhihe Digital Technology Co. Ltd Beijing China Shijiazhuang Municipal Party Committee Municipal Government Decision-Making Advisory Committee ShijiaZhuang China

ISBN: (纸本)9798350332919

Aiming at the problems of long development period, tedious development of multiple views, high update and maintenance costs, the paper proposes the design method of party construction visualization large screen based on componentization and builds a party construction data visualization system. In this paper, we take the party construction data of a university in Hebei Province as an example, firstly, we describe the basic process of data processing and visualization, then we describe the componentized way to build the chart components applicable to the party construction scenario, and finally we propose a 'V-C' (Views-Components) component interaction method for to improve the interactivity of the multi-view visualization screen. the interactivity of the large visualization screen. through testing, the system has a friendly interface, easy to operate, and convenient to build a large visualization screen for party building data, which is convenient to understand the dynamics of daily party building work in time and play a role in reminding and warning. © 2023 IEEE.

关键词： Data visualization

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Cultivating Customer Purchase Intent: Leveraging Machine Learning for Precise Predictions 12

Cultivating Customer Purchase Intent: Leveraging Machine Lea...

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12th international conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Krishna, R. Dhanush Mahadev, M. Hariprasad, S. Abhishek, S. Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350369861

Insurance firms must employ cost-effective client engagement tactics in order to maximize the effectiveness of their marketing campaigns in the ever-changing business landscape of today. this work uses machine learning to solve the problem of effectively identifying future insurance subscribers. To build a prediction model, we utilize past marketing data from a modern insurance provider. the model makes use of historical campaign data, such as call duration, frequency of contact, and previous campaign outcomes, as well as client variables including age, job type, marital status, educational background, and contact communication style. through the analysis of these characteristics, our machine learning model predicts the probability of a customer subscribing to the insurance product, allowing the business to focus on high-potential customers and reduce the expense of *** addition to offering insightful information about consumer behavior, the research helps to improve the general effectiveness of marketing tactics used by the insurance sector. © 2023 IEEE.

关键词： Insurance

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Image recognition using Kullback-Leibler information discrimination

Image recognition using Kullback-Leibler information discrim...

引用

international Workshop on Soft computing Applications and Knowledge Discovery 2011, SCAKD 2011 - In Conjunction with the 4th international conference on pattern recognition and Machine Intelligence, PReMI 2011

作者： Savchenko, Andrey National Research University Higher School of Economics B. Pecherskaya St. 25/12 603155 Nizhniy Novgorod Russia

the problem of automatic image recognition based on the minimum information discrimination principle is formulated and solved. Color histograms comparison in the Kullback-Leibler information metric is proposed. It's combined with method of directed enumeration alternatives as opposed to complete enumeration of competing hypotheses. Results of an experimental study of the Kullback-Leibler discrimination in the problem of face recognition with a large database are presented. It is shown that the proposed algorithm is characterized by increased accuracy and reliability of image recognition.

关键词： Face recognition

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A Study of Prefrontal Cortex Task Switching Using Spiking Neural Networks 12

A Study of Prefrontal Cortex Task Switching Using Spiking Ne...

引用

12th international conference on Advanced Computational Intelligence (ICACI)

作者： Viswanathan, K. Ashwin Mylavarapu, Goutam Chen, Kun thomas, Johnson P. Oklahoma State Univ Dept Compute Sci Stillwater OK 74078 USA

ISBN: (纸本)9781728142487

In this work we study the behavior of Prefrontal Cortex (PFC) and understand its role in task switching by developing a biologically based computational model. We build the PFC neurons using Spiking Neural Networks (SNN) with biologically realizable features having lateral inhibition, synaptic weight changes using unsupervised Spike Timing Dependant Plasticity (STDP) learning rule, spiking threshold and biological ranges for neuronal parameter values. the SNN is composed of Leaky Integrate and Fire (LIF) neurons which are efficient to model and represents the Excitatory neurons in Glutamate layer and Inhibitory neurons in GABA layer. In this implementation we use two real world datasets as tasks for the PFC network to learn. We demonstrate the switching behavior of the neurons and their synaptic weight adaptations by formulating experiments in a manner consistent with real world trials used in the study of cognitive psychology. Using these experiments we show how our model adapts and responds to task changes exhibiting biological behaviors like Long Term Potentiation (LTP), Long Term Depression (LTD) and Task-set reconfiguration (TSR) thereby giving insights into understanding the importance of duration between changing tasks and its effect on performance and efficacies of multi-tasking. the results shown in this paper relate favorably well with the natural neuronal responses found in the brain.

关键词： Spiking Neural Network neuroscience cognitive modeling STDP unsupervised learning cognitive science pattern recognition cognitive computing Computational neuroscience

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Efficient Regression with Feature Selection Based on L12-Norm 4

Efficient Regression with Feature Selection Based on L12-Nor...

引用

4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Lei, Yu Li, Qin You, Jane Shenzhen Institute of Information Technology School of Software Engineering Shenzhen China Xidian University School of Telecommunications Engineering Xi'an China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (纸本)9798350324303

Feature selection is one of the most important components of many machine learning applications. Most of the existing sparse coding-based methods only concerned with the regularization of the transformation matrix, but ignoring the sparsity of projected features in the intrinsic feature space. In this paper, we introduce a novel regularization with 12-norm, called class specificity distribution term. It aims to ensure that samples with the same label could obey a specific distribution, while data from the different categories should have different distributions in the intrinsic feature space. then we propose a novel embedded feature selection framework based on regression and class specificity distribution term measured by 12-norm, termed RL12. An efficient augmented Lagrange multipliers based optimization algorithm is proposed to solve the 12-norm regularization iteratively. Experimental results demonstrate the effectiveness of our method. © 2023 IEEE.

关键词： Feature Selection

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High-Performance computing Systems and Technologies in Scientific Research, Automation of Control and Production 1

引用

丛书名： Communications in Computer and Information Science

1000年

作者： Vladimir Jordan Ilya Tarasov Ella Shurina Nikolay Filimonov Vladimir Faerman

ISBN: (数字)9783031237447

ISBN: (纸本)9783031237430

this book constitutes the refereed proceedings of the 12th international conference on High-Performance computing Systems and Technologies in Scientific Research, Automation of Control and Production, HPCST 2022, held in Barnaul, Russia, during May 20–21, 2022.;the 23 full papers included in this book were carefully reviewed and selected from 116 submissions. they were organized in topical sections as follows: hardware for high-performance computing and signal processing; information technologies and computer simulation of physical phenomena; computing technologies in data analysis and decision making; and computing technologies in information security applications.

关键词： Computer Systems Organization and Communication Networks Mathematics of computing Computer Imaging, Vision, pattern recognition and Graphics Artificial Intelligence Data Structures and Information theory Software Engineering/Programming and Operating Systems

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Discrete circular arcs

Discrete circular arcs

引用

international conference on pattern recognition

作者： M. Worring A.W.M. Smeulders Faculty of Mathematics and Computer Science University of Amsterdam Netherlands

In this contribution we consider in all detail the effect of digitization on circular arcs. Given a specific discrete circular arc we find the set of all continuous arcs which by digitization would result in this pattern. From this characterization we provide optimal estimates of the radius (or curvature) of the original arc. this estimator achieves the ultimate precision one can reach in estimation which we call the geometric minimum variance bound (GMVB).

关键词： Shape Stochastic systems Stochastic resonance Mathematics Computer science High-resolution imaging Quantization Spatial resolution Image resolution Grid computing

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Brain Informatics 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Peipeng Liang Vinod Goel Chunlei Shan

ISBN: (数字)9783030370787

ISBN: (纸本)9783030370770

this book constitutes the refereed proceedings of the 12th international conference on Brain Informatics, BI 2019, held in Haikou, China, in December 2019.;the 26 revised full papers were carefully reviewed and selected from 36 submissions. the papers are organized in the following topical sections: cognitive and computational foundations of brain science; human information processing systems; brain big data analysis, curation and management; informatics paradigms for brain and mental health research; and brain-machine intelligence and brain-inspired computing. Also included is a special session on computational social analysis for mental health.

关键词： Artificial Intelligence Data Mining and Knowledge Discovery Models and Principles Computer Imaging, Vision, pattern recognition and Graphics

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