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Tilted Least-Squares Parameter Estimation of Linear Regression Models in the Presence of Outliers

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Tilted Least-Squares Parameter Estimation of Linear Regressi...

IEEE Conference on Decision and Control

作者： Biqiang Mu Er-Wei Bai Wei Xing Zheng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering University of Iowa Iowa City Iowa USA School of Computer Data and Mathematical Sciences Western Sydney University Sydney NSW Australia

The least squares estimator is the most popular identification method. In the absence of prior knowledge on the unknown noise, uniform weights on all samples are often as-sumed. In reality, potentially unknown contamination is always present and the uniform weights are not necessarily the best. Further, explicit information about the nature of contamination is usually absent. To this end, a relaxed-tilted least squares method is proposed here to assign unequal weights so that the effect of undesired noise contamination can be mitigated. The relaxed-tilted least squares method tilts the uniform prior on the samples so as to move the uniform distribution in a direction that enjoys the smallest estimation error in the neighborhood of the uniform distribution. Theoretical results are established including the ability of outlier removal and the guaranteed parameter convergence in the presence of outliers. Numerical algorithms are proposed and simulated, which support the theoretical derivations.

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AutoSTL: Automated Spatio-Temporal Multi-Task Learning

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arXiv 2023年

作者： Zhang, Zijian Zhao, Xiangyu Miao, Hao Zhang, Chunxu Zhao, Hongwei Zhang, Junbo College of Computer Science and Technology Jilin University China School of Data Science City University of Hong Kong Hong Kong Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China Department of Computer Science Aalborg University Denmark JD Intelligent Cities Research China JD iCity JD Technology China Hong Kong Institute for Data Science City University of Hong Kong Hong Kong

Spatio-temporal prediction plays a critical role in smart city construction. Jointly modeling multiple spatio-temporal tasks can further promote an intelligent city life by integrating their inseparable relationship. However, existing studies fail to address this joint learning problem well, which generally solve tasks individually or a fixed task combination. The challenges lie in the tangled relation between different properties, the demand for supporting flexible combinations of tasks and the complex spatio-temporal dependency. To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly. Firstly, we propose a scalable architecture consisting of advanced spatio-temporal operations to exploit the complicated dependency. Shared modules and feature fusion mechanism are incorporated to further capture the intrinsic relationship between tasks. Furthermore, our model automatically allocates the operations and fusion weight. Extensive experiments on benchmark datasets verified that our model achieves state-of-the-art performance. As we can know, AutoSTL is the first automated spatio-temporal multi-task learning method. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart city

Wind Speed Prediction Based on ARMA and SVR

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Wind Speed Prediction Based on ARMA and SVR

data Driven Control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Qinmin Yang Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

Wind power generation technology is one of the research hotspots of renewable energy nowadays. In order to ensure the stable and reliable operation of wind power generation equipment, wind speed prediction is very important. This paper provides a new idea for the wind speed prediction based on Autoregressive Moving Average (ARMA) and Support Vector Regression (SVR). First, to reasonably divide the original data into multiple historical data with strong correlation as features to predict the future wind speed, the ARMA model is employed and its partial autocorrelation coefficient is calculated. By this means, the input features can be optimally selected and the training set of the prediction model can be constructed. Further, SVR model is used to build the nonlinear relationship between the input features and future wind speed. Finally, through simulation, it proves that this method saves more time than try and error method in selecting input features, and through comparison with Backpropagation Neural Network (BPNN), it proves that this method can achieves higher wind speed prediction accuracy.

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Digital Twinning of a Pressurized Water Reactor Startup Operation and Partial Computational Offloading in In-network Computing-Assisted Multiaccess Edge Computing

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arXiv 2024年

作者： Aliyu, Ibrahim Arigi, Awwal M. Um, Tai-Won Kim, Jinsul The Department of Intelligent Electronics and Computer Engineering Chonnam National University Gwangju61186 Korea Republic of The Department of Humans and Automation Institute for Energy Technology Halden1777 Norway The Graduate School of Data Science Chonnam National University Gwangju61186 Korea Republic of

This paper addresses the challenge of representing complex human action (HA) in a nuclear power plant (NPP) digital twin (DT) and minimizing latency in partial computation offloading (PCO) in sixth-generation-enabled computing in the network (COIN) assisted multiaccess edge computing (MEC). Accurate HA representation in the DT-HA model is vital for modeling human interventions that are crucial for the safe and efficient operation of NPPs. In this context, DT-enabled COIN-assisted MEC harnesses DT (known as a cybertwin) capabilities to optimize resource allocation and reduce latency effectively. A two-stage approach is employed to address system complexity. First, a probabilistic graphical model (PGM) is introduced to capture HAs in the DT abstraction. In the PGM, HA and NPP asset-twin abstractions form coupled systems that evolve and interact through observable data and control input. Next, the underlying PCO problem is formulated as a multiuser game, where NPP assets can partially offload tasks to COIN and MEC. We propose a decentralized algorithm to optimize offloading decisions, offloading ratios, and resource allocation. The simulation results demonstrate the effectiveness of the proposed method in capturing complex HAs and optimal resource allocation in DT-enabled NPPs. Copyright © 2024, The Authors. All rights reserved.

关键词： Nuclear power plants

NODLINK: An Online System for Fine-Grained APT Attack Detection and Investigation

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arXiv 2023年

作者： Li, Shaofei Dong, Feng Xiao, Xusheng Wang, Haoyu Shao, Fei Chen, Jiedong Guo, Yao Chen, Xiangqun Li, Ding School of Computer Science Peking University China Arizona State University United States Case Western Reserve University United States Sangfor Technologies Inc. China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

Advanced Persistent Threats (APT) attacks have plagued modern enterprises, causing significant financial losses. To counter these attacks, researchers propose techniques that capture the complex and stealthy scenarios of APT attacks by using provenance graphs to model system entities and their dependencies. Particularly, to accelerate attack detection and reduce financial losses, online provenance-based detection systems that detect and investigate APT attacks under the constraints of timeliness and limited resources are in dire need. Unfortunately, existing online systems usually sacrifice detection granularity to reduce computational complexity and produce provenance graphs with more than 100,000 nodes, posing challenges for security admins to interpret the detection results. In this paper, we design and implement NODLINK, the first online detection system that maintains high detection accuracy without sacrificing detection granularity. Our insight is that the APT attack detection process in online provenance-based detection systems can be modeled as a Steiner Tree Problem (STP), which has efficient online approximation algorithms that recover concise attack-related provenance graphs with a theoretically bounded error. To utilize the frameworks of the STP approximation algorithm for APT attack detection, we propose a novel design of in-memory cache, an efficient attack screening method, and a new STP approximation algorithm that is more efficient than the conventional one in APT attack detection while maintaining the same complexity. We evaluate NODLINK in a production environment. The open-world experiment shows that NODLINK outperforms two state-of-the-art (SOTA) online provenance analysis systems by achieving magnitudes higher detection and investigation accuracy while having the same or higher throughput. Copyright © 2023, The Authors. All rights reserved.

关键词： Approximation algorithms

IoT-Driven Image Recognition for Microplastic Analysis in Water Systems using Convolutional Neural Networks

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IoT-Driven Image Recognition for Microplastic Analysis in Wa...

computer, Communication and Control (IC4), International Conference on

作者： M D Ashfaqul Hasan K. Balasubadra G. Vadivel N. Arunfred M.V Ishwarya S. Murugan Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Information Technology R.M.D. Engineering College Kavaraipettai Chennai Tamil Nadu India Department of Information Technology University of Technology and Applied Science Salalah Dhofar Oman Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Microplastic pollution in water systems is growing, requiring novel detection and analysis methods. This research presents an Internet of Things (IoT)-driven image identification system using Convolutional Neural Networks (CNNs) to detect and quantify microplastics in water samples. The suggested method is more scalable and responsive due to IoT real-time data capture and remote monitoring of water infrastructure. An innovative CNN architecture for image processing allows the system to accurately identify micro plastics. The CNN model is trained and validated using a large dataset of micro plastic-containing water samples. The trained model can recognize various sizes, shapes, and colors of micro plastics, making it responsive to different environmental situations. The IoT architecture also allows image recognition modules in dispersed sensor nodes to cover water systems. Extensive studies prove the system can analyze vast amounts of image data quickly and reliably. Edge computing also minimizes latency and improves micro plastic analysis system responsiveness. The suggested IoT-driven image recognition method for continuous micro plastic pollution monitoring and evaluation in water systems seems promising. Scalability, realtime capabilities, and accuracy make it useful for environmental monitoring agencies and academics trying to reduce microplastics’ influence on aquatic ecosystems. This system advances IoT applications in environmental and pollution management.

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Machine Learning Method for the Diagnosis of Retinal Diseases using Convolutional Neural Network

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Machine Learning Method for the Diagnosis of Retinal Disease...

data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Sumit Kushwaha Jayaram Boga Bairaboina Sai Sambasiva Rao Syed Noeman Taqui R G Vidhya J. Surendiran Department of Computer Applications University Institute of Computing Chandigarh University Punjab India Department of Computer science and Data science Marri Laxman Reddy Institute of Technology and Management Hyderabad Telangana India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of ECE HKBK College of Engineering Bangalore India

The early diagnosis of visual problems such as diabetic retinopathy and glaucoma relies on the successful completion of medical image processing tasks such as the segmentation of retinal blood vessels. The process of isolating retinal blood vessels from images of the retinal fundus is a challenging one because of the similarities in intensity that exist between blood vessels and the surrounding tissues. In addition, there are differences in the size, shape, and intensity of the vessels. In recent times, a number of different algorithms based on deep learning have been presented for the segmentation of retinal blood vessels. These techniques have been shown to be more accurate and dependable when compared to the traditional computer vision methods that have been used. In this abstract, we present the U-Net deep learning model as well as a technique for segmenting retinal blood vessels that is based on the programming language Python. A U-Net model is trained using a series of retinal fundus images so that the features of retinal blood vessels may be comprehended. As part of the inference process, the image of the retinal fundus is loaded into a trained version of the U-Net model. This model then generates a probability map that illustrates the chance that each pixel represents an artery in the human body. The final segmentation is produced by applying a threshold to the probability map. When it comes to the process of segmenting retinal blood vessels, our python-based solution outperforms methods that are considered to be state-of-the-art in studies comparing accuracy and speed. The scientific and medical communities have unrestricted access to the software that may be used to put the suggested strategy into action since it is an open-source initiative. In conclusion, the python-based approach for retinal blood vessel segmentation that makes use of U-Net provides a potentially helpful solution for identifying retinal illnesses and has the potential to serve as a stan

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DAGKT: Difficulty and Attempts Boosted Graph-based Knowledge Tracing

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arXiv 2022年

作者： Luo, Rui Liu, Fei Liang, Wenhao Zhang, Yuhong Bu, Chenyang Hu, Xuegang Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China School of Computer Science and Information Engineering Hefei University of Technology China Jianzai Tech Hefei China

In the field of intelligent education, knowledge tracing (KT) has attracted increasing attention, which estimates and traces students’ mastery of knowledge concepts to provide high-quality education. In KT, there are natural graph structures among questions and knowledge concepts so some studies explored the application of graph neural networks (GNNs) to improve the performance of the KT models which have not used graph structure. However, most of them ignored both the questions’ difficulties and students’ attempts at questions. Actually, questions with the same knowledge concepts have different difficulties, and students’ different attempts also represent different knowledge mastery. In this paper, we propose a difficulty and attempts boosted graph-based KT (DAGKT)3, using rich information from students’ records. Moreover, a novel method is designed to establish the question similarity relationship inspired by the F1 score. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed DAGKT. Copyright © 2022, The Authors. All rights reserved.

关键词： Students

Deep Insights of Learning based Micro Expression Recognition: A Perspective on Promises, Challenges and Research Needs

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TechRxiv

TechRxiv 2022年

作者： Verma, Monu Vipparthi, Santosh Kumar Singh, Girdhari Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur302017 India Mehta Family School of Data Science and Artificial Intelligence IIT Guwahati781039 India

Micro expression recognition (MER) is a very challenging area of research due to its intrinsic nature and fine-grained changes. In the literature, the problem of MER has been solved through handcrafted/descriptor-based techniques. However, in recent times, deep learning (DL) based techniques have been adopted to gain higher performance for MER. Also, rich survey articles on MER are available by summarizing the datasets, experimental settings, conventional and deep learning methods. In contrast, these studies lack the ability to convey the impact of network design paradigms and experimental setting strategies for DL based MER. Therefore, this paper aims to provide a deep insight into the DL-based MER frameworks with a perspective on promises in network model designing, experimental strategies, challenges, and research needs. Also, the detailed categorization of available MER frameworks is presented in various aspects of model design and technical characteristics. Moreover, an empirical analysis of the experimental and validation protocols adopted by MER methods is presented. The challenges mentioned earlier and network design strategies may assist the affective computing research community in forge ahead in MER research. Finally, we point out the future directions, research needs and draw our conclusions. © 2022, CC BY.

关键词： Face recognition