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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

Uncertainty estimation with recursive feature machines 24

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Uncertainty estimation with recursive feature machines

Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Daniel Gedon Amirhesam Abedsoltan Thomas B. Schön Mikhail Belkin Department of Information Technology Uppsala University Sweden Department of Computer Science and Engineering UC San Diego Department of Computer Science and Engineering UC San Diego and Halıcıoğlu Data Science Institute UC San Diego

In conventional regression analysis, predictions are typically represented as point estimates derived from covariates. The Gaussian Process (GP) offer a kernel-based framework that predicts and quantifies associated uncertainties. However, kernelbased methods often underperform ensemble-based decision tree approaches in regression tasks involving tabular and categorical data. Recently, Recursive Feature Machines (RFMs) were proposed as a novel feature-learning kernel which strengthens the capabilities of kernel machines. In this study, we harness the power of these RFMs in a probabilistic GP-based approach to enhance uncertainty estimation through feature extraction within kernel methods. We employ this learned kernel for in-depth uncertainty analysis. On tabular datasets, our RFM-based method surpasses other leading uncertainty estimation techniques, including NGBoost and CatBoost-ensemble. Additionally, when assessing out-of-distribution performance, we found that boosting-based methods are surpassed by our RFM-based approach.

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ADDRESSING THE NULL PARADOX IN EPIDEMIC MODELS: CORRECTING FOR COLLIDER BIAS IN CAUSAL INFERENCE

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arXiv 2024年

作者： Bong, Heejong Ventura, Valérie Wasserman, Larry Department of Statistics University of Michigan Ann ArborMI48109 United States Department of Statistics & Data Science Carnegie Mellon University PittsburghPA15213 United States

We address the null paradox in epidemic models, where standard methods estimate a non-zero treatment effect despite the true effect being zero. This occurs when epidemic models mis-specify how causal effects propagate over time, especially when covariates act as colliders between past interventions and latent variables, leading to spurious correlations. Standard approaches like maximum likelihood and Bayesian methods can misinterpret these biases, inferring false causal relationships. While semi-parametric models and inverse propensity weighting offer potential solutions, they often limit the ability of domain experts to incorporate epidemic-specific knowledge. To resolve this, we propose an alternative estimating equation that corrects for collider bias while allowing for statistical inference with frequentist guarantees, previously unavailable for complex models like SEIR. © 2024, CC BY.

关键词： Inverse problems

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting

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arXiv 2025年

作者： Benechehab, Abdelhakim Feofanov, Vasilii Paolo, Giuseppe Thomas, Albert Filippone, Maurizio Kégl, Balázs Huawei Noah’s Ark Lab Paris France Department of Data Science EURECOM Statistics Program KAUST Saudi Arabia

Pre-trained foundation models (FMs) have shown exceptional performance in univariate time series forecasting tasks. However, several practical challenges persist, including managing intricate dependencies among features and quantifying uncertainty in predictions. This study aims to tackle these critical limitations by introducing adapters—feature-space transformations that facilitate the effective use of pre-trained univariate time series FMs for multivariate tasks. Adapters operate by projecting multivariate inputs into a suitable latent space and applying the FM independently to each dimension. Inspired by the literature on representation learning and partially stochastic Bayesian neural networks, we present a range of adapters and optimization/inference strategies. Experiments conducted on both synthetic and real-world datasets confirm the efficacy of adapters, demonstrating substantial enhancements in forecasting accuracy and uncertainty quantification compared to baseline methods. Our framework, AdaPTS, positions adapters as a modular, scalable, and effective solution for leveraging time series FMs in multivariate contexts, thereby promoting their wider adoption in real-world applications. We release the code at https://***/abenechehab/AdaPTS. © 2025, CC BY.

关键词： Stochastic systems

Optimization techniques for preserving privacy in data mining 5

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Optimization techniques for preserving privacy in data minin...

5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Devi, K.Renuka Balasamy, K. Prathyusha, M. Jeevitha, R. Balasubramanie, P. Eswaran, Malathi Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam India MVSR Engineering College Department of IT Hyderabad India KPR Institute of Engineering and Technology Department of CSE Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode Perundurai India

ISBN: (纸本)9781665493604

data mining is one of the significant area where it plays a predominant role in extracting important factors and trends from large volume of data. This covers various areas such as healthcare, education, entertainment, finance, e-commerce applications etc., The data mining domain has used a variety of algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning techniques. Under healthcare arena, it deals with huge amount of sensitive data such as patients' data such as their name, age, health records. Those sensitive data have been utilized by the intruders for extracting the original data and also became a prey for the authorized access. Hence, the privacy is one of the serious concern that should be addressed. Various privacy preserving in data mining (PPDM) techniques such as anonymization, perturbation, condensation and cryptographic methods are available to protect those data. In this paper, the optimization techniques such as Genetic algorithm(GA) under evolutionary method and Particle swarm optimization(PSO) under meta heuristic method have been discussed and how it plays an important part in providing more optimal results by securing those sensitive and important information from the unauthorized access. © 2023 IEEE.

关键词： Genetic algorithms

Causal health impacts of power plant emission controls under modeled and uncertain physical process interference

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arXiv 2023年

作者： Wikle, Nathan B. Zigler, Corwin M. Department of Statistics and Actuarial Science University of Iowa United States Department of Statistics and Data Sciences University of Texas Austin United States

Causal inference with spatial environmental data is often challenging due to the presence of interference: outcomes for observational units depend on some combination of local and non-local treatment. This is especially relevant when estimating the effect of power plant emissions controls on population health, as pollution exposure is dictated by (i) the location of point-source emissions, as well as (ii) the transport of pollutants across space via dynamic physical-chemical processes. In this work, we estimate the effectiveness of air quality interventions at coal-fired power plants in reducing two adverse health outcomes in Texas in 2016: pediatric asthma ED visits and Medicare all-cause mortality. We develop methods for causal inference with interference when the underlying network structure is not known with certainty and instead must be estimated from ancillary data. Notably, uncertainty in the interference structure is propagated to the resulting causal effect estimates. We offer a Bayesian, spatial mechanistic model for the interference mapping which we combine with a flexible non-parametric outcome model to marginalize estimates of causal effects over uncertainty in the structure of interference. Our analysis finds some evidence that emissions controls at upwind power plants reduce asthma ED visits and all-cause mortality, however accounting for uncertainty in the interference renders the results largely inconclusive. Copyright © 2023, The Authors. All rights reserved.

关键词： Coal fired power plant

Abnormal Load Patterns Detection in Smart Grids Using Temporal Convolutional Neural Network Based Gated Recurrent Units Networks with Multi-Head Temporal Attention 4

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Abnormal Load Patterns Detection in Smart Grids Using Tempor...

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Balassem, Zayd Srikanteswara, Ramya Esakki Madura, E. Anjaneyulu, Madugula Devi, M. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science India Gokaraju Rangaraju Institute of Engineering and Technology Department of Artificial Intelligence and Machine Learning Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350352931

In recent era, the advancement in smart grid technologies and the integration of renewable energy sources have modernized the power distribution landscape. The efficiency and stability of overall system is ensured by the controlling of power converters. In existing, Support Vector Machine (SVM) is utilized but it assumes the normal data follows Gaussian distribution which is not be the same for smart grid data. In this research, Temporal Convolutional Neural Network based Gated Recurrent Units Networks with Multi-Head Temporal Attention (TCN-GRU-MTA) is proposed. Initially, the input signals are considered from Smart Grid Stability (SGS) dataset. Then, the signals are further preprocessed with autoencoders to reduce noise and to improve quality of signals. The Siamese Neural Networks (SNN) feature extractor efficiently learns relevant features from smart grid data which are indicative of abnormal behavior. After that, the SNN is introduced for extracting features from preprocessed data. Next, the proposed TCN-GRU-MTA is utilized to detect the abnormal load patterns in smart grid networks. From the results, the proposed-established better results including accuracy (99.10%), precision (89.98%), recall (99.05%) and F1-score (95.20%) when compared to existing Quadratic SVM. © 2024 IEEE.

关键词： Smart power grids

Redundancy-Free Self-Supervised Relational Learning for Graph Clustering

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arXiv 2023年

作者： Yi, Si-Yu Ju, Wei Qin, Yifang Luo, Xiao Liu, Luchen Zhou, Yong-Dao Zhang, Ming School of Statistics and Data Science Nankai University Tianjin300071 China School of Computer Science Peking University Beijing100871 China Department of Computer Science University of California Los Angeles90095 United States

Graph clustering, which learns the node representations for effective cluster assignments, is a fundamental yet challenging task in data analysis and has received considerable attention accompanied by graph neural networks in recent years. However, most existing methods overlook the inherent relational information among the non-independent and non-identically distributed nodes in a graph. Due to the lack of exploration of relational attributes, the semantic information of the graph-structured data fails to be fully exploited which leads to poor clustering performance. In this paper, we propose a novel self-supervised deep graph clustering method named Relational Redundancy-Free Graph Clustering (R2FGC) to tackle the problem. It extracts the attribute- and structure-level relational information from both global and local views based on an autoencoder and a graph autoencoder. To obtain effective representations of the semantic information, we preserve the consistent relation among augmented nodes, whereas the redundant relation is further reduced for learning discriminative embeddings. In addition, a simple yet valid strategy is utilized to alleviate the over-smoothing issue. Extensive experiments are performed on widely used benchmark datasets to validate the superiority of our R2FGC over state-of-the-art baselines. Our codes are available at https://***/yisiyu95/R2FGC. © 2023, CC BY.

关键词： Redundancy

Advances in Clustering Algorithms for Large-Scale data Processing with AI 3

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Advances in Clustering Algorithms for Large-Scale Data Proce...

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sharma, Sonal Singh, Vandana Jeyalaxmi, M. Chaudhari, Prasad B. Das, Neeraj Garg, Avni Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

Clustering is a widely used technique in statistics mining for exploring and reading records with AI. It is an unsupervised gaining knowledge of technique, which may be used for exploratory facts evaluation. Clustering primarily aims to group similar statistics factors in a given facts set. In recent years, diverse advances in clustering algorithms have been developed for massive-scale record processing with AI. Some of the maximum popular clustering algorithms are ok-means, hierarchical clustering, fuzzy clustering, and spectral clustering. This kind of algorithm was used with AI for massive-scale records processing. Okay-approach and hierarchical clustering algorithms were widely used in diverse information mining responsibilities. They permit one to discover and organize the maximum similar statistics points. Fuzzy clustering and spectral clustering algorithms have become famous recently because of their capability to duplicate human perception and questioning when clustering records. Lately, deep learning techniques have been used with clustering algorithms to enhance their overall performance and boom the accuracy of records evaluation. Deep mastering is a subset of machine mastering which allows computers to research from repeated exposure to big datasets robotically. It permits clustering algorithms to investigate big-scale facts faster and more as it should be. Clustering with AI is an effective method for processing and analyzing huge-scale records. Recent advances in clustering algorithms and deep mastering strategies have improved performance and accuracy. Moreover, these algorithms may be used in diverse applications, which include client segmentation, community evaluation, and anomaly detection. © 2023 IEEE.

关键词： algorithms applications performance processing segmentation