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The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional data? 25

The Curse Revisited: When are Distances Informative for the ...

引用

international conference on Artificial Intelligence and Statistics

作者： Vandaele, Robin Kang, Bo De Bie, Tijl Saeys, Yvan Univ Ghent Dept Appl Math Comp Sci & Stat Ghent Belgium VIB Inflammat Res Ctr Data Min & Modelling Biomed DaMBi Ghent Belgium Univ Ghent Dept Elect & Informat Syst IDLab Ghent Belgium

Distances between data points are widely used in machine learning applications. Yet, when corrupted by noise, these distances-and thus the models based upon them-may lose their usefulness in high dimensions. Indeed, the small marginal effects of the noise may then accumulate quickly, shifting empirical closest and furthest neighbors away from the ground truth. In this paper, we exactly characterize such effects in noisy high-dimensional data using an asymptotic probabilistic expression. Previously, it has been argued that neighborhood queries become meaningless and unstable when distance concentration occurs, which means that there is a poor relative discrimination between the furthest and closest neighbors in the data. However, we conclude that this is not necessarily the case when we decompose the data in a ground truth-which we aim to recover-and noise component. More specifically, we derive that under particular conditions, empirical neighborhood relations affected by noise are still likely to be truthful even when distance concentration occurs. We also include thorough empirical verification of our results, as well as interesting experiments in which our derived 'phase shift' where neighbors become random or not turns out to be identical to the phase shift where common dimensionality reduction methods perform poorly or well for recovering low-dimensional reconstructions of high-dimensional data with dense noise.

关键词： Clustering algorithms

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Design of Brand Positioning and Market Analysis System Based on Visual Communication

Design of Brand Positioning and Market Analysis System Based...

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Liao Sha College of Art and Design Xiangnan University Chenzhou China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

With the intensification of market competition and the increasing diversity of consumer demands, scientific brand positioning and visual image design are becoming increasingly important for enterprises' brand marketing strategies. This study aims to construct a brand positioning and marketing system integrating visual design, market analysis, and decision support based on visual communication theory and big data analysis technology. The system, implemented through a distributed microservices architecture, realizes advantages such as high performance and scalability. The visual design module utilizes deep learning technology to automatically generate personalized visual identification schemes, while the data analysis module integrates multiple data sources for mining and analysis. The decision engine integrates the outputs of both modules to form comprehensive brand visual images and marketing strategy recommendations. The system has been validated through multiple practical application cases, significantly enhancing enterprises' brand image and marketing performance. In the future, the system will continue to be optimized and upgraded, exploring new models of integration between visual and data analysis to bring innovative practices to the field of brand marketing.

关键词： Visualization data analysis Visual communication Scalability Soft sensors Education Microservice architectures Information processing Big data Engines

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A Virtual Network Mapping Method Based on Compound Particle Swarm Optimization 7

A Virtual Network Mapping Method Based on Compound Particle ...

引用

7th international conference on Cyber Security and Information Engineering, ICCSIE 2022

作者： Huang, Jianhua Tang, Yunlong Wei, Yongjun Wang, Huan Zhang, Haifeng Zhang, Bing School of Computer Science and Technology School of Software Guangxi University of Science and Technology Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Guangxi Education System Network Security Monitoring Center Liuzhou China Liuzhou Railway Vocational Technical College Liuzhou China

ISBN: (纸本)9781665455749

Aiming at problems of low credibility of results and low accuracy of the scheme in the mapping process of virtual networks. It is suggested to use a composite particle swarm optimization approach for virtual network mapping. Firstly, to derive the maximum number of virtual cyberspaces, we construct the virtual network model and the physical network model;secondly, resource limitations are used to build the node mapping and link mapping models, and once more, the mapping scheme of the virtual network on the physical network is solved using the composite particle swarm algorithm to improve the acceptance rate of virtual network requests. In simulation experiments, we compare our established algorithm with two algorithms, D-ViNE-SP and VNE-R-PSO, in two dimensions, and the results show that our proposed VNE-MM-PSO mapping method improves the acceptance rate of network requests by 8% and also improves on the benefit-cost ratio. © 2022 IEEE.

关键词： Particle swarm optimization (PSO)

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Research on Text Error Correction Algorithm Design in English Writing Aid System for Non-Native Speakers

Research on Text Error Correction Algorithm Design in Englis...

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Ying Liu Beijing Polytechnic Beijing China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

This paper studies the design of text error correction algorithm in the English writing aid system for nonnative speakers based on neural network algorithm, aiming at improving the English writing level and text quality of nonnative speakers. First, this paper analyzes the common types of mistakes made by non-native speakers in English writing, including vocabulary, grammar, spelling and punctuation errors. Based on these error types, this paper designs a text error correction algorithm based on neural network, which combines deep learning technology and natural language processing method. The algorithm uses models such as convolutional neural network (CNN) and recurrent neural network to detect and correct text errors at multiple levels. Through the training of a large number of labeled data, the model can effectively capture the complex structure and semantic relations in the language, and achieve high-precision error recognition and correction. This paper uses a large corpus of English learners to carry out experiments, and the results show that the algorithm based on neural network is significantly better than the traditional rule-based and statistical methods in terms of error recognition and correction accuracy and recall rate.

关键词： Training Deep learning Vocabulary Recurrent neural networks Statistical analysis Writing Natural language processing data models Error correction Convolutional neural networks

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FEDIC: Federated Learning on Non-IID and Long-Tailed data via Calibrated Distillation

FEDIC: Federated Learning on Non-IID and Long-Tailed Data vi...

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2022 IEEE international conference on Multimedia and Expo, ICME 2022

作者： Shang, Xinyi Lu, Yang Cheung, Yiu-Ming Wang, Hanzi Xiamen University China Hong Kong Baptist University Hong Kong

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Federated learning provides a privacy guarantee for generating good deep learning models on distributed clients with different kinds of data. Nevertheless, dealing with non-IID data is one of the most challenging problems for federated learning. Researchers have proposed a variety of methods to eliminate the negative influence of non-IIDness. However, they only focus on the non-IID data provided that the universal class distribution is balanced. In many real-world applications, the universal class distribution is long-tailed, which causes the model seriously biased. Therefore, this paper studies the joint problem of non-IID and long-tailed data in federated learning and proposes a corresponding solution called Federated Ensemble Distillation with Imbalance Calibration (FEDIC). To deal with non-IID data, FEDIC uses model ensemble to take advantage of the diversity of models trained on non-IID data. Then, a new distillation method with logit adjustment and calibration gating network is proposed to solve the long-tail problem effectively. We evaluate FEDIC on CIFAR-10-LT, CIFAR-100-LT, and ImageNet-LT with a highly non-IID experimental setting, in comparison with the state-of-the-art methods of federated learning and long-tail learning. Our code is available at https://***/shangxinyi/FEDIC. © 2022 IEEE.

关键词： Distillation

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Automating a Breakthrough Technology for Real-Time Diagnostics: Sealed Wellbore Pressure Monitoring

Automating a Breakthrough Technology for Real-Time Diagnosti...

引用

2022 SPE Hydraulic Fracturing Technology conference and Exhibition, HFTC 2022

作者： Ramirez, Alberto Basic, Damir Merritt, Josh Olson, Karen Van Domelen, Mary Well Data Labs

ISBN: (纸本)9781613998540

The upstream oil and gas industry has seen its share of innovation over the past several decades. The driving force behind these changes has always been a relentless push toward operational and capital efficiency. A breakthrough patented pressure diagnostic technique using offset sealed wellbores as monitoring sources was introduced at the 2020 Hydraulic Fracturing Technology conference (Haustveit et al. 2020). This technique quantifies various hydraulic fracture parameters using only a surface gauge mounted on the sealed wellbore. The authors successfully automated the Sealed Wellbore Pressure Monitoring (SWPM) analysis procedure using a cloud-based analytical platform (CBAP) designed to ingest, process, and analyze high-frequency hydraulic fracturing data (Iriarte et al. 2021b). The minimum data for the analysis consists of the standard frac treatment data combined with the high-resolution pressure gauge data for each sealed wellbore. The team developed machine learning algorithms to identify the key events required by a sealed wellbore pressure analysis: the start, end, and magnitude of each pressure response detected in the sealed wellbore while actively fracturing offset wells. The result is a rapid, repeatable SWPM analysis that minimizes individual interpretation biases. Since then, over 10,000 stages have been analyzed with SWPM in every major North and South American unconventional basin. The next logical step in the process was to move from post-treatment to real-time analyses. This required an extensive data set to train the real-time models. The training data set includes two types of data: active well data including treating pressures and slurry rates for 1000+ stages from all major North American basins;and 2500+ hours of monitoring well pressure and temperature data streams. The authors use signal processing techniques to mitigate noise, easily accommodate business rules, and follow the subject matter experts' decision logic. The data is combined with

关键词： Oil field equipment

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An Evaluation of LDA Modeling in Financial News Articles

An Evaluation of LDA Modeling in Financial News Articles

引用

international conference on Artificial Intelligence and Sustainable Engineering, AISE 2020

作者： Katarya, Rahul Das, Indrajeet Shrivastava, Bhanu Keswani, Karan Department of Computer Science and Engineering Delhi Technological University Delhi India

ISBN: (纸本)9789811685415

In the knowledge-oriented world of today, details related to any particular topic are readily available to humans in many forms. In some scenarios like financial time series prediction, new information like policy changes, changing trends, etc. becomes the front-runners in effecting the stock market. Also, it is not entirely feasible to predict stock market trends through the use of existing machine learning techniques as these techniques rely heavily on historical data, and stock market trends rarely behave in resonance with historical data. This paper attempts to evaluate LDA and a variation of LDA using performance metrics, namely perplexity and coherence. These words extracted using the above models can be used as features for various algorithms that aim to predict stock market trends through the blend of both historical data and new information. Also, a table, in the end, illustrates the words generated per topic from the application of variation of LDA. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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Transfer Learning Approaches for Improved Thyroid Detection

Transfer Learning Approaches for Improved Thyroid Detection

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： D. M. Kalai Selvi Kireet Joshi Vani V Daxa Vekariya Natrayan L Harshal Patil Department of Computer Science and Engineering R.M.D Engineering College Chennai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Information Science and Engineering Bangalore Institute of Technology Bangalore Karnataka India Department of Computer Science & Engineering Parul institute of Engineering and Technology Parul University Post Limda Gujarat India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Thyroid disease is a broad term for any medical disorder that impairs the thyroid gland's ability to generate enough hormones. It is not limited to any particular age group; it can strike anyone at any time. Detecting thyroid diseases usually includes blood tests, which can be difficult to interpret due to the large amount of data required for accurate predictions. To find the best algorithm for predicting thyroid risk, this study examines different Transfer Learning (TL) methods. The dataset utilized for this work was sourced from the machine learning repository at the University of California, Irvine. The distribution of thyroid class labels in this dataset is highly imbalanced. To address this, sampling techniques are used to balance the dataset. Transfer learning models like AlexNet, Inception, and MobileNet are employed. All models achieve an accuracy rate of 97%. Upon comparison, AlexNet demonstrates the highest accuracy among the three models. The research shows that proper preprocessing and data balancing can improve thyroid detection efficiency.

关键词： Surveys Accuracy Sensitivity Machine learning algorithms Transfer learning Prediction algorithms data mining Thyroid Diseases Biomedical imaging

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Research on Prediction of Medical & IT Sectors in A-shares Based on Random Forest Algorithm

Research on Prediction of Medical & IT Sectors in A-shares B...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Kunrui Chen Lei Yang Shipei Du Longqing Zhang Yungui Chen Liwei Tian School of Computer Science Guangdong University of Science and Technology Dongguan China Dongguan Key Laboratory of Industrial Software for Intelligent Manufacturing Guangdong University of Science and Technology Dongguan China Guangdong University of Science and Technology Dongguan China Dongguan Key Laboratory of Kunpeng Computing School of Computer Science Guangdong University of Science and Technology Dongguan China Guangdong University of Science and Technology Dongguan Key Laboratory of Kunpeng Computing Dongguan China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

This research is set against the backdrop of China's Shanghai Stock Exchange A-share market, selecting three representative stocks from each of two distinct sectors as the research objects. Leveraging the “orange” software, we employed the Random Forest model to predict the closing prices of stocks belonging to these two sectors. The findings indicate that, with respect to these two diverse sectors, the Random Forest model yielded no discernible difference in prediction outcomes.

关键词： Analytical models Accuracy Computational modeling Predictive models Reliability engineering data models Software Stock markets Predictive analytics Random forests

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Gestational weight gain prediction using privacy preserving federated learning 43

Gestational weight gain prediction using privacy preserving ...

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43rd Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Puri, Chetanya Dolui, Koustabh Kooijman, Gerben Masculo, Felipe Van Sambeek, Shannon Den Boer, Sebastiaan Michiels, Sam Hallez, Hans Luca, Stijn Vanrumste, Bart Katholieke Univ Leuven Dept Elect Engn ESAT eMedia Lab Leuven Belgium Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS Leuven Belgium Katholieke Univ Leuven imec DistriNet Leuven Belgium Philips Res Eindhoven Netherlands Univ Ghent Dept Data Anal & Math Modelling Ghent Belgium

ISBN: (纸本)9781728111797

Gestational weight gain prediction in expecting women is associated with multiple risks. Manageable interventions can be devised if the weight gain can be predicted as early as possible. However, training the model to predict such weight gain requires access to centrally stored privacy sensitive weight data. Federated learning can help mitigate this problem by sending local copies of trained models instead of raw data and aggregate them at the central server. In this paper, we present a privacy preserving federated learning approach where the participating users collaboratively learn and update the global model. Furthermore, we show that this model updation can be done incrementally without having the need to store the local updates eternally. Our proposed model achieves a mean absolute error of 4.455 kgs whilst preserving privacy against 2.572 kgs achieved in a centralised approach utilising individual training data until day 140. Clinical relevance- Privacy preserving training of machine learning algorithm for early gestational weight gain prediction with minor tradeoff to performance.

关键词： Training Pregnancy Privacy data privacy Machine learning algorithms Training data Predictive models

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