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IEEE International Conference on Big Data

作者： Hassan Eldeeb Shota Amashukeli Radwa ElShawi Institute of Computer Science University of Tartu Tartu Estonia Computers and Automatic Control Department Faculty of Engineering Tanta University Tanta Egypt.

ISBN: (纸本)9781665480468

Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features’ quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat.

关键词： Measurement Industries Scalability Pipelines Buildings Machine learning Big Data

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Resource Allocation for RIS-Assisted Device-to-Device Communications in Heterogeneous Cellular Networks

arXiv

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arXiv 2023年

作者： Ao, Shaoyou Niu, Yong Han, Zhu Ai, Bo Zhong, Zhangdui Wang, Ning Qiao, Yuanyuan The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Information Engineering Zhengzhou University Zhengzhou450001 China

In recent years, with the explosive growth of data traffic, communication base stations (BSs) need to serve more and more users. Offloading traffic from BSs has become an efficient way to reduce the burden on BSs. Device-to-Device (D2D) communications have emerged to improve spectrum utilization by reusing the frequency spectrum of the cellular frequency band. In the general environment, Heterogeneous Cellular Networks (HCNs) including millimeter wave (mm-wave) have appeared. Since the D2D link allows to share of spectrum resources with the cellular user, it will bring potential interference to the cellular user. Fortunately, an emerging technology called Reconfigurable Intelligent Surface (RIS) can mitigate the severe interference caused by D2D links by shaping the incident beam and improving the multipath phase shift. In this paper, we study the resource allocation scheme to maximize the system sum rate, in the RIS-assisted single-cell heterogeneous D2D communication scenario. To solve the Block Coordinate Descent (BCD) problem, the problem of maximizing the sum rate is decomposed into three sub-problems. The resource allocation sub-problem is solved by a coalitional game method based on the game theory. The power allocation problem of the coalition converts the concave function into a convex optimization by mathematical transformation. The problem is solved by the gradient descent method. The local search method is adopted to find the optimum for the phase conversion problem. Then iterate until the difference of sum rate is less than the threshold. The simulation results show that the designed algorithm is superior to other benchmark schemes in the literature. Copyright © 2023, The Authors. All rights reserved.

关键词： Resource allocation

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Unsupervised Learning for Wafer Surface Defect Pattern Recognition

Unsupervised Learning for Wafer Surface Defect Pattern Recog...

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2021中国智能自动化大会(CIAC 2021)

作者： Sheng Geng Huaping Liu Feng Wang Shimin Zhao Hu Liu Department of Control Science and Engineering Shanghai Institute of Technology Department of Computer Science and Technology Tsinghua University

With the development of the semiconductor industry, the demand for wafer production has gradually increased. Wafer manufacturing is a very complicated process, and any abnormal fluctuations in each process in this process may cause surface defects on the *** and rapid identification of wafer defect patterns can promptly reflect abnormal problems in the production process. Aiming at such tasks, this paper proposes a pattern recognition method for wafer surface defects based on unsupervised learning. The method is divided into two parts: unsupervised pre-training and classification fine-tuning. In the unsupervised pre-training stage, in order to improve the model's surface defect feature extraction capability, we propose a new wafer-oriented unsupervised sampling method(WaUSM). Our wafer surface defect pattern recognition method uses an unsupervised pre-training model as the initialization to set up a classification model downstream, and fine-tune the classification model through a small number of label images with wafer defect patterns, so that the classification model can more accurately identify various types of defects pattern. The method we proposed was successfully verified by wafer defect pattern recognition on the public dataset WM-811K, and the experimental results fully proved the effectiveness and industrial applicability of the method we proposed.

关键词： Semiconductor Wafer defect Unsupervised learning Pattern recognition

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Design and Deployment of a Simulation Platform: Case Study of an Agent-Based Model for Youth Suicide Prevention

Design and Deployment of a Simulation Platform: Case Study o...

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Simulation Winter Conference

作者： Joshua Huddleston Michael C. Galgoczy Kareem A. Ghumrawi Philippe J. Giabbanelli Ketra L. Rice Nisha Nataraj Margaret M. Brown Christopher R. Harper Curtis S. Florence Department of Computer Science & Software Engineering Miami University Oxford OH USA National Center for Injury Prevention and Control Centers for Disease Control and Prevention (CDC) Atlanta GA USA

ISBN: (纸本)9781665476621

Research has examined the process of engaging end-users in co-designing a simulation model and using it within workplace settings. In contrast, few studies document the software development aspect of creating, packaging, and deploying a simulation platform that satisfies end-users' needs. In this case study, we detail these aspects regarding a platform involving Agent-Based Modeling for youth suicide prevention. Goals and constraints are detailed in three categories of needs, each showing how competing demands are addressed via software solutions: 1) data, encompassing data security (e.g., no direct access to individual-level data) and data updates (e.g., changing a model's initialization without coding expertise); 2) deployment, to satisfy security protocols (e.g., secure intranet communications) and the end-users' experience (e.g., ease of installation); 3) accessibility, ensuring that individuals with impairments are not excluded from using simulations. By presenting practical solutions to each category, our case study supports modelers in addressing their own deployment needs.

关键词： Protocols Filtering Employment Ecosystems Packaging Software Data models

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Effect of lead time prediction accuracy in trust-based resource sharing

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IFAC-PapersOnLine 2021年第1期54卷 1126-1131页

作者： Ádám Szaller Botond Kádár Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Nowadays, challenges are changing production facilities: decentralized production networks are replacing centralized organizations to remain competitive. This paper investigates a resource sharing approach where matching resource offers and requests are made by an intermediate platform. One of the main pillars of collaboration is to keep promises, especially about deadlines: in the presented model, facilities could rate each other based on trustfulness and choose from offers based on this setting. Lead time prediction accuracy has a direct effect on the real processing intervals: if the prediction was accurate, the deadline could be met, which results in good ratings and a higher possibility to win more jobs. In the paper, effect of lead time prediction accuracy is investigated in trust-based resource sharing, and the performance of facilities is compared with agent-based simulation.

关键词： Co-operative control/manufacturing Multi-agent simulation Manufacturing System engineering

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Network-based analysis to uncover drug-induced adverse side-effects

Network-based analysis to uncover drug-induced adverse side-...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Alessio Funari Paola Paci Federica Conte Department of Translational and Precision Medicine Sapienza University of Rome Rome Italy Institute for Systems Analysis and Computer Science "Antonio Ruberti" National Research Council Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Karolinska Institutet Stockholm Sweden

Despite the drug approval process consists of extremely rigorous clinical and preclinical studies, not all side effects are identified before its marketing, posing a significant risk to public health. Furthermore, considering the huge use of economic and human resources, in-silico predictive approaches for the identification of side effects are essential. In this study, we introduce a new method based on random walk with restart algorithm to delineate previously unidentified links between drugs and side effects, and we apply it on the drug-induced Asthma and long QT syndrome. We identified the genes potentially involved in the development of the analyzed side effect by comparing side-effect-related drugs with drugs not known to induce side effects. Analyzing the sets of genes most likely influenced by the perturbation of each individual drug, we observed that, on average, side-effect-related drugs perturb a higher percentage of genes involved in the development of side effects compared to side-effect-unrelated drugs. Based on this finding, we developed a classifier to explore all possible unknown associations between drugs and side effects. This method can be extended to the analysis of other side effects as well.

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DANet: Density Adaptive Convolutional Network with Interactive Attention for 3D Point Clouds

arXiv

引用

arXiv 2023年

作者： He, Yong Yu, Hongshan Yang, Zhengeng Sun, Wei Feng, Mingtao Mian, Ajmal The National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University Lushan South Rd. Yuelu Dist. Changsha410082 China The School of Computer Science and Technology Xidian University Xi’an710071 China The Department of Computer Science The University of Western Australia PerthWA6009 Australia

Local features and contextual dependencies are crucial for 3D point cloud analysis. Many works have been devoted to designing better local convolutional kernels that exploit the contextual dependencies. However, current point convolutions lack robustness to varying point cloud density. Moreover, contextual modeling is dominated by non-local or self-attention models which are computationally expensive. To solve these problems, we propose density adaptive convolution, coined DAConv. The key idea is to adaptively learn the convolutional weights from geometric connections obtained from the point density and position. To extract precise context dependencies with fewer computations, we propose an interactive attention module (IAM) that embeds spatial information into channel attention along different spatial directions. DAConv and IAM are integrated in a hierarchical network architecture to achieve local density and contextual direction-aware learning for point cloud analysis. Experiments show that DAConv is significantly more robust to point density compared to existing methods and extensive comparisons on challenging 3D point cloud datasets show that our network achieves state-of-the-art classification results of 93.6% on ModelNet40, competitive semantic segmentation results of 68.71% mIoU on S3DIS and part segmentation results of 86.7% mIoU on ShapeNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Segmenting Alzheimer's Disease MRI Slices for Hippocampal Volume Calculation

Segmenting Alzheimer's Disease MRI Slices for Hippocampal Vo...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Hsiao-Chuan Tan Gilang Titah Ramadhan Wei-Cheng Shen Wen-Thong Chang Chih-Yang Lin Ben-Yi Liau Yih-Kuen Jan Yori Pusparani Chi-Wen Lung Department of Artificial Intelligence PhD Program Asia University Taichung Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Creative Product Design Asia University Taichung Taiwan Department of Mechanical Engineering National Central University Taoyuan Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Kinesiology and Community Health University of Illinois Urbana America Department of Digital Media Design Asia University Taichung Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

Alzheimer's is a neurodegenerative disease that quietly steals human memory. This study analyzed hippocampal volume in Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Normal Cognition (NC) using MRI image slices from the ADNI database and YOLOv8 instance segmentation. We used 300 images to segment the left and right hippocampal. This study focused on volume calculation from five MRI image slices. The results showed 0.98 (AD), 0.92 (MCI), and 0.90 (NC) and revealed significant volumetric differences showed in the NC class (P = 0.045). This suggests early neuroanatomical changes can occur without cognitive symptoms, highlighting the potential of deep learning in the early detection of neurodegenerative diseases.

关键词： Instance segmentation Deep learning Accuracy Databases Magnetic resonance imaging Cognition Alzheimer's disease

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Distributionally Robust control for Chance-Constrained Signal Temporal Logic Specifications

Distributionally Robust Control for Chance-Constrained Signa...

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IEEE Conference on Decision and control

作者： Arash Bahari Kordabad Eleftherios E. Vlahakis Lars Lindemann Dimos V. Dimarogonas Sadegh Soudjani Max Planck Institute for Software Systems Kaiserslautern Germany Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science Viterbi School of Engineering University of Southern California Los Angeles USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We consider distributionally robust optimal control of stochastic linear systems under signal temporal logic (STL) chance constraints when the disturbance distribution is unknown. By assuming that the underlying predicate functions are Lipschitz continuous and the noise realizations are drawn from a distribution having a concentration of measure property, we first formulate the underlying chance-constrained control problem as stochastic programming with constraints on expectations and propose a solution using a distributionally robust approach based on the Wasserstein metric. We show that by choosing a proper Wasserstein radius, the original chance-constrained optimization can be satisfied with a user-defined confidence level. A numerical example illustrates the efficacy of the method.

关键词： Linear systems Robust control Noise Optimal control Transforms Programming Probabilistic logic Trajectory Noise measurement Optimization

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, computer Retrospective Study

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