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9th International Conference on big data computing and Communications, bigCom 2023

作者： Wu, Xiaodong Wang, Ronghai Lin, Guoxin Quanzhou Normal University School of Mathmatics and Computer Science China Fujian Province University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian China Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

ISBN: (纸本)9798350331240

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms. © 2023 IEEE.

关键词： Virtual machine

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Using Rough Sets to Improve the High-dimensional data Anomaly Detection Method Based on Extended Isolation Forest 26

Using Rough Sets to Improve the High-dimensional Data Anomal...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Hanlin Zhou, Jiantao Li, Hua Inner Mongolia University College of Computer Science Hohhot China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods. © 2023 IEEE.

关键词： Anomaly detection

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Judgment of blast furnace iron-tapping status based on data differential processing and dynamic window analysis algorithm

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Progress in Natural Science:Materials International 2023年第4期33卷 450-457页

作者： Youcheng Zong Zixian Wang Xiang Liu Yi Nian Jianian Pan Chaojie Zhang Yingxue Wang Jianhua Chu Liqiang Zhang The Intelligent Metallurgy Research Institute School of Metallurgical Engineering Anhui University of Technology Department of Technical Quality Manufacturing Management Division(Technical Center) Anhui Changjiang Steel Co. Ltd. National Engineering Research Center for Public Safety Risk Perception and Control by Big Data(RPP) China Academic of Electronics and Information Technology

Ironmaking plants hold a core position in steel production. Efficient level detection and iron-tapping status judgment can accurately measure the liquid level and predict the amount of molten iron in the ladle, thereby ensuring production safety and improving production efficiency. At present, the judgment of the start and end statuses of blast furnace iron-tapping mainly relies on manual visual inspection. However, due to the high-temperature environment on-site and interference from iron powder, gunning mud, and other dust, operators find it challenging to make accurate judgments. To address these issues, this study proposes a robust and accurate method for judging the iron-tapping status of blast furnaces. This study collected four million on-site liquid level data, underwent interpolation, noise reduction, normalization, and other preprocessing steps, and then used differential processing to calculate the iron-tapping rate. Combined with dynamic window feature analysis, it effectively captured the change characteristics of the liquid level in the ladle under different iron-tapping *** simulated on-site experiments, the method achieved a 96.6 % accuracy rate for iron-tapping status *** real-time iron-tapping status judgment method based on differential processing of liquid level data and dynamic window feature analysis proposed in this study demonstrates high precision and robustness in the complex environment of ironmaking plants. This technology not only improves the production efficiency of the iron and steel link but also frees on-site operators from the harsh working environment, eliminating human errors in judgments and omissions, further promoting the intelligent process of ironmaking plants.

关键词： Blast furnace iron-tapping Molten iron level Radar level gauge Iron-tapping status judgment data differential processing Sliding window

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Low Profile, High Aperture Efficiency Magneto-Electric Dipole Circularly Polarized Beam Scanning Antenna Array

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IEEE Antennas and Wireless Propagation Letters 2025年第5期24卷 1094-1098页

作者： Wang, Chao Xu, Guanghui Meng, Yanfeng Yan, Jiaxin Xie, Guoda Chen, Yi Wang, Wei Zhao, Luyu Li, Minquan Li, Yingsong Huang, Zhixiang Anhui University The Industry-Education-Research Institute of Advanced Materials and Technology for Integrated Circuits Information Materials and Intelligent Sensing Laboratory of Anhui Province Center for Big Data and Population Health of lHM Hefei230601 China East China Research Institute of Electronic Engineering Hefei230088 China

In this letter, a low-profile, high-aperture-efficiency millimeter-wave magneto-electric dipole (MED) circularly polarized (CP) beam-scanning antenna array is proposed. Initially, a novel MED antenna element is designed by arranging metallized vias around the radiating patches to serve as beam directors. This novel method not only provides broad axial-ratio (AR) beams and wide half-power beamwidths but also maintains a low-profile and compact structural characteristic. By adjusting the positions of the via directors, the radiation beam direction of the antenna element is effectively controlled. Subsequently, 4 × 4 antenna arrays for the xoz and yoz planes are designed and simulated to verify the CP beam scanning performance. The results demonstrate that the maximum CP beam scanning range can reach from -50° to +50°, with the AR maintained below 3 dB throughout the scanning range. At 29 GHz, the peak aperture efficiencies of the antenna arrays reach 73.8%, while maintaining a low profile of just 0.12λ0. Finally, the measurement results confirm the validity of the simulations. © 2002-2011 IEEE.

关键词： Millimeter waves

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Multi-scale cyclical similarity prototype refinement for few-shot breast ultrasound image segmentation 17

Multi-scale cyclical similarity prototype refinement for few...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Ou, Yingfeng Yang, Xing Zhang, Jian Jian, Caiqing Wang, Lihui College of Computer Science and Technology Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data Engineering Research Center of Text Computing Ministry of Education Guiyang China

ISBN: (纸本)9798350387384

Few-shot learning based methods can address the reliance on large-scale labeled samples in current breast tumor segmentation. However, previous methods typically rely on a few support samples to extract abstract, coarse-grained semantic prototypes, without considering the interaction between support and query data. In this study, we aim to provide each query image with relevant and information-rich support information. By integrating the difficult point selection module and the multi-scale cyclical prototype refinement module, we progressively refine coarse-grained support prototypes into more informative, fine-grained class prototypes, thereby better matching the highly variable breast ultrasound query data. Experiments on two datasets demonstrate that our proposed multi-scale cyclical similarity prototype refinement network (CSPRNet) achieves remarkable improvements compared to previous state-of-the-art methods. Specifically, using ResNet50 as the backbone, we achieve 57.93%/57.92% and 57.58%/59.24% mIoU for 1-shot and 5-shot segmentation, respectively, on public and private datasets, outperforming the suboptimal method by 8.18%/2.55% and 2.77%/4.48%. © 2024 IEEE.

关键词： Ultrasonic imaging

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High-Performance and Resource-Efficient Dynamic Memory Management in High-Level Synthesis 24

High-Performance and Resource-Efficient Dynamic Memory Manag...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wang, Qinggang Zheng, Long An, Zhaozeng Huang, Haoqin Zhu, Haoran Huang, Yu Yao, Pengcheng Liao, Xiaofei Jin, Hai National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China

ISBN: (纸本)9798400706011

With the merits of high productivity and ease of use, highlevel synthesis (HLS) tools bring hope to fast FPGA-based architecture development. However, their usability and popularity are still limited due to lack of support for dynamic memory management (DMM). Though HLS-compatible DMM solutions have been proposed recently, nevertheless, based on our investigation, none of them can hit high performance (i.e., minimal memory (de-)allocation latency) and resource efficiency (i.e., managing arbitrarily sized memory with minimal FPGA resource consumption) with one stone, seriously limiting their practicality. In response, we propose HeroDMM, a high-performance and resource-efficient dynamic memory manager for HLS. Specifically, HeroDMM organizes the managed memory area with a novel cartesian-like tree (CT) structure, a key to resolving the dilemma between (de-)allocation latency and resource efficiency standing in front of prior efforts. With the CT structure, HeroDMM further devises a delicate memory management algorithm and specializes the hardware implementation for achieving ever-higher performance while ensuring resource efficiency. Results show that HeroDMM outperforms state-of-the-art HLS-compatible DMM solutions by 61.69%∼99.99% in performance improvement and 23.79%∼97.22% in resource consumption savings. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Memory management

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Evolution of the Participation of Women in University Computer Science Studies in Spain and in Europe 53

Evolution of the Participation of Women in University Comput...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Nebot, Àngela Mugica, Francisco Soft Computing Research Group Intelligent Data Science and Artificial Intelligence Research Center Universitat Politécnica de Catalunya Barcelona Spain

ISBN: (纸本)9798350336429

This paper presents a comprehensive examination of the current state of underrepresentation of women in computer science (CS) degrees in Spain. It offers an overview of the situation across autonomous communities in Spain, highlighting the evolving trends within the country, regional variations, and the distinctions between public and private universities. The research employs descriptive statistical methods to analyze data obtained from the Spanish Ministry of Universities, Eurostat, as well as recent literature reviews. Furthermore, the study contextualizes the evolution of female enrollments in Spain by comparing it with data from other European countries. The primary objective is to gain a thorough understanding of the broader Spanish landscape and suggest future research directions aimed at reducing the gender gap in CS studies. © 2023 IEEE.

关键词： computer science degree descriptive statistical methods gender imbalance

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A NOVEL RULE-BASED OVERSAMPLING APPROACH FOR IMBALANCED data CLASSIFICATION 37

A NOVEL RULE-BASED OVERSAMPLING APPROACH FOR IMBALANCED DATA...

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37th Annual European Simulation and Modelling Conference, ESM 2023

作者： Zhang, Xiao Paz, Iván Nebot, Àngela Soft Computing Research Group Intelligent Data Science and Artificial Intelligence Research Center Universitat Politècnica de Catalunya Barcelona Spain

ISBN: (纸本)9789492859280

When confronted with imbalanced datasets, traditional classifiers frequently struggle to correctly categorize samples from the minority class, adversely impacting the overall predictive performance of machine learning models. Current oversampling techniques generally focus on data interpolation through neighbor selection, often neglecting to uncover underlying data structures and relationships. This study introduces a novel application for RuLer, an algorithm originally developed for identifying sound patterns in the artistic domain of live coding. When adapted for data oversampling (as Ad-RuLer), the algorithm shows significant promise in addressing the challenges associated with imbalanced class distribution. We undertake a thorough comparative evaluation of Ad-RuLer against established oversampling algorithms such as SMOTE, ADASYN, Tomek-links, Borderline-SMOTE, and KmeansSMOTE. The evaluation employs various classifiers including logistic regression, random forest, and XGBoost, and is conducted over six real-world biomedical datasets with varying degrees of imbalance. © 2023 ESM. All Rights Reserved.

关键词： Classification data synthesis Imbalanced data Oversampling Rule-based approach

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research on Blockchain-based Decentralized Federated Learning

Research on Blockchain-based Decentralized Federated Learnin...

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2023 International Conference on Computer Applications Technology, CCAT 2023

作者： Han, Boyuan Liang, Zhihong Qin, Mingming Jiang, Rongxin Dai, Wei Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China Southwest Forestry University Big Data and Cloud Computing Center Kunming China Yunnan Pu'er Technical School Yunnan Pu'er Technical School Kunming China

ISBN: (纸本)9798350301540

The integration of blockchain and federated learning exhibits a highly synergistic relationship. Blockchain, with its assurance of trustworthiness in computational processes and data, combined with federated learning's ability to make data available yet invisible, mutually complement each other and have shown initial advancements across various domains. Consequently, research and analysis of blockchain-based federated learning technology have garnered considerable *** blockchain enhances the federated learning process, it also poses a number of problems, such as increased communication overhead and lack of computational *** article aims to provide a comprehensive examination of the subject. Firstly, it introduces the fundamental concept of federated learning technology, along with privacy protection techniques and the existing threat landscape. Secondly, it analyzes the core principles of blockchain-based federated learning technology, delving into current research status, progress, and outlining the various research directions involved. Furthermore, it conducts an in-depth analysis and summarization of the integration between federated learning and blockchain technology, identifying numerous challenges that need to be addressed within the relevant domains. Finally, it recognizes and analyzes the future development of this combination, emphasizing the need to overcome existing *** summary, this article highlights the amalgamation of federated learning and blockchain technology. It identifies and analyzes the many challenges that require resolution within the pertinent fields, while also offering insights into the future development of this integration. © 2023 IEEE.

关键词： Blockchain

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Sampled-data Synchronization of Quaternion-valued Neural Networks by Non-decomposition Method 8

Sampled-data Synchronization of Quaternion-valued Neural Net...

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8th International Symposium on Computer Science and intelligent Control, ISCSIC 2024

作者： Li, Ning Li, Hanyin He, Dongyu Wang, HeYizhong Henan University of Economics and Law The College of Mathematics and Information Science Zhengzhou China Henan University of Economics and Law Research Center of Intelligent Transportation and Big Data Zhengzhou China

ISBN: (纸本)9798350380286

This paper studies sampled-data control of quaternion-valued neural networks(QVNNs) with variable sampling, based on the quaternion matrices nondecomposition method. Firstly, the synchronization error model of quaternion-valued neural networks is established. Then, by adopting a refined input delay approach, and structuring a discontinuous Lyapunov function, the quaternion-valued LMI synchronization criteria for QVNNs are obtained. Furthermore, the proposed quaternion-valued LMIs are transformed into complexvalued LMIs equivalently. In addition, the event-trigger synchronization criteria are easily obtained by introducing the event-trigger scheme. Finally, the obtained results are demonstrated by a numerical simulation. © 2024 IEEE.

关键词： Input delay approach Non-decomposition method Quaternion-valued neural networks Variable sampling

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