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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1412-1434页

作者： Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsuChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Software Northwestern Polytechnical UniversityXianShaanxiChina Department of Computer Science School of PhysicsEngineering&Computer ScienceUniversity of HertfordshireHatfieldUK Department of AI and Software Gachon University Seongnam-siSeongnamSouth Korea

Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in software engineering,and iTrust electronic Health Care System.

关键词： data sources machine learning requirement engineering supervised learning algorithms

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Detection of healthy and pathological heartbeat dynamics in ECG signals using multivariate recurrence networks with multiple scale factors

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Chinese Physics B 2023年第10期32卷 273-282页

作者：马璐陈梅辉何爱军程德强杨小冬 School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Suzhou Vocational and Technical College Suzhou 234000China School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China

The electrocardiogram(ECG)is one of the physiological signals applied in medical clinics to determine health *** physiological complexity of the cardiac system is related to age,disease,*** the investigation of the effects of age and cardiovascular disease on the cardiac system,we then construct multivariate recurrence networks with multiple scale factors from multivariate time *** propose a new concept of cross-clustering coefficient entropy to construct a weighted network,and calculate the average weighted path length and the graph energy of the weighted network to quantitatively probe the topological *** obtained results suggest that these two network measures show distinct changes between different *** is because,with aging or cardiovascular disease,a reduction in the conductivity or structural changes in the myocardium of the heart contributes to a reduction in the complexity of the cardiac ***,the complexity of the cardiac system is *** that,the support vector machine(SVM)classifier is adopted to evaluate the performance of the proposed *** of 94.1%and 95.58%between healthy and myocardial infarction is achieved on two ***,this method can be adopted for the development of a noninvasive and low-cost clinical prognostic system to identify heart-related diseases and detect hidden state changes in the cardiac system.

关键词： electrocardiogram signals multivariate recurrence networks cross-clustering coefficient entropy multiscale analysis

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A Compact Broadband Filtering Circularly Polarized Antenna with Band-pass Structure in Sequential-phase Feed Network

A Compact Broadband Filtering Circularly Polarized Antenna w...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Wan, Jia Cao, Xin Zhou, Longjian Cai, Qiangming Chen, Cong Zhu, Yuyu Southwest University of Science and Technology School of Information Engineering Mianyang China South China University of Technology School of Electronic and Information Engineering Guangzhou China

ISBN: (纸本)9798350375909

A compact broadband filtering circularly polarized antenna with wide bandwidth and high frequency selectivity using serially configured band-pass filtering structure in sequential-phase feed network is proposed. The antenna consists of a feed network containing a filtering structure and two circular patches with parasitic strips and notches. The parasitic strips and notches are loaded to make the axial ratio smaller and obtain better circularly polarized radiation performance. To expand the axial ratio bandwidth and impedance bandwidth, a sequential phase feed network is designed, and a band-pass filtering structure is integrated on it to achieve high frequency selectivity. The filtering structure of the antenna is serially configured by four quarter-wavelength branches, and the four branches are symmetrically distributed on both sides of the small microstrip line in the middle. The impedance bandwidth of the designed antenna can reach 38% (2.81 GHz to 4.13 GHz) and axial ratio bandwidth can reach 36% (2.8 GHz to 4.06 GHz). The result of the highest in-band right-hand circular polarization (RHCP) realized gain is 6.8 dBi and the filtering structure can achieve a gain suppression of more than 25 dB in the upper stopband and more than 30 dB in the lower stopband. © 2024 IEEE.

关键词： Bandwidth

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Fuzzy Twin Support Vector Machine Based on Smooth Pinball Loss

Fuzzy Twin Support Vector Machine Based on Smooth Pinball Lo...

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2023 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2023

作者： Zheng, Zaihong Lu, Peng Cai, Hongbin Li, Ping School of Electronic and Information Engineering University of Science and Technology Liaoning Liaoning China School of Electronic and Information Engineering University of Science and Technology Liaoning China

ISBN: (纸本)9798350309003

Twin support vector machine (TWSVM) is constructed based on hinge loss function and least squares loss function. And in TWSVM, all training samples are given the same importance. Thus, TWSVM is sensitive to noise, and is instability to re-sampling. To overcome the issue, a Fuzzy TWSVM with smooth Pinball loss (Sp-FTSVM) is proposed in this paper. By defining the smooth Pinball loss, Sp-FTSVM deals with the quantile distance, which reduces its sensitivity to noise. To further weaken the sensitivity to noise, different confidence levels are assigned to the samples by using fuzzy memberships. Lastly, the numerical experiments on original UCI datasets, corrupted UCI datasets with noise and steel surface defects dataset verify the feasibility and effectiveness of the Sp-FTSVM. © 2023 IEEE.

关键词： Support vector machines

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Self-adaptive label filtering learning for unsupervised domain adaptation

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Frontiers of Computer science 2023年第1期17卷 225-227页

作者： Qing TIAN Heyang SUN Shun PENG Tinghuai MA School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing 210044China

1 Introduction As an emerging machine learning paradigm,unsupervised domain adaptation(UDA)aims to train an effective model for unlabeled target domain by leveraging knowledge from related but distribution-inconsistent source *** of the existing UDA methods[2]align class-wise distributions resorting to target domain pseudo-labels,for which hard labels may be misguided by misclassifications while soft labels are confusing with trivial noises so that both of them tend to cause frustrating *** overcome such drawbacks,as shown in Fig.1,we propose to achieve UDA by performing self-adaptive label filtering learning(SALFL)from both the statistical and the geometrical perspectives,which filters out the misclassified pseudo-labels to reduce negative ***,the proposed SALFL firstly predicts labels for the target domain instances by graph-based random walking and then filters out those noise labels by self-adaptive learning strategy.

关键词： filtering resort overcome

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PA2Net: Pyramid Attention Aggregation Network for Saliency Detection 31st

PA2Net: Pyramid Attention Aggregation Network for Saliency ...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Yu, Jizhe Liu, Yu Wu, Xiaoshuai Xu, Kaiping Li, Jiangquan DUT School of Software Technology and DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9789819620630

With the development of deep convolutional neural networks (CNNs), salient object detection has become increasingly mature. Existing methods primarily enhance model performance by deepening and widening U-shaped networks. However, these methods typically consider the top-level features of bottom-up processes as global context, which is suboptimal and limited. This limitation arises because existing CNNs do not consider long-range dependencies, focusing more on local convolution operations. Additionally, the implicit interaction between complementary features in the top-down process fails to effectively learn the structural properties and semantic information, and it inevitably introduces significant noise. To address these issues, this paper adopts the idea of attention mechanisms to restore the object integrity in a more explicit manner while avoiding the introduction of irrelevant factors. First, we design a Global Context Enhancement (GCE) module to further enhance object localization and global context modeling by learning the correlations among deep features. Subsequently, an attention-based Pyramid Aggregation Decoder progressively merges multi-level adjacent features by learning their pairwise correspondences, gradually learning local details and global representations. Furthermore, a Noise Filter Block (NF) is integrated into the PAD to suppress background noise interference. Experimental results on five benchmark datasets show that our model performs favorably compared to 15 state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Frequency-,radiation pattern-and polarization-reconfigurable antenna for wireless applications

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The Journal of China Universities of Posts and Telecommunications 2023年第1期30卷 87-92页

作者： Bai Xia Su Ming Wang Qingmin Liu Yang Gao Zhiwei School of Information Science and Technology Shijiazhuang Tiedao UniversityShijiazhuang 050043China School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

In this communication,a frequency-,radiation pattern-and polarization-reconfigurable antenna employing liquid metal is *** crossed dipole antennas are surrounded by four independent reflectors and directors to realize multi-beam *** length of dipole arms can be adjusted by extracting the liquid metal from the needle tube to achieve frequency *** polarization can be switched by injecting liquid metal into different dipole microfluidic *** is simple in design and has multiple reconfigurable *** antenna with a relative frequency tuning range of 35.8%extending from 2.43 GHz to 3.49 GHz is *** also can perform 6 kinds of beam steering over a 360°coverage and switch between two different *** antenna has potential to employ cognitive radio(CR)and base station in wireless systems.

关键词： reconfigurable antenna liquid metal crossed dipoles reflectors compound reconfiguration

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A Comprehensive Data Augmentation Method for Modern Cigarette Packaging Defect Detection 7

A Comprehensive Data Augmentation Method for Modern Cigarett...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Ma, Yanjin Rao, Yunbo Xue, Junmin He, Qixue School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Chengdu Information Technology of Chinese Academy of Sciences Co. Ltd Chengdu China

ISBN: (纸本)9798350350890

This study addresses the issue of insufficient quantity and incomplete types of defect detection datasets for cigarette packages in modern industrial production line scenarios. We propose a cigarette packaging defect data augmentation method. The method consists of three components: preprocessing for obtaining cigarette package defect data through segmentation and cropping based on Segment Anything Model(SAM), generation of cigarette package defects using Cigarette Box Defect-Improved Denoising Diffusion Probabilistic Model (CBD-IDDPM), and We expanded the original dataset by utilizing 600 generated images and trained different defect detection models using the augmented dataset. The experimental results validate the effectiveness of our dataset augmentation method for cigarette package defect detection. The mAP50-95 of YOLOv8n has increased from 73% to 75.2%. © 2024 IEEE.

关键词： CBD-IDDPM data augmentation defect detection image generation

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Optimal social welfare:A many-to-many data transaction mechanism based on double auctions

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Digital Communications and Networks 2023年第5期9卷 1230-1241页

作者： Jingyuan Duan Ling Tian Jianqiao Mao Jiaxin Li School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Shenzhen Institute of Information Technology China Department of Electronic and Electrical Engineering University College LondonGower StreetLondonUnited Kingdom Amazon(China)Investment Ltd China

With the development of Big Data and the Internet of Things(IoT),the data value is more significant in both academia and *** can achieve maximal data value and prepare data for smart city *** to data's unique characteristics,such as dispersion,heterogeneity and distributed storage,an unbiased platform is necessary for the data trading market with rational trading ***,there are multiple buyers and sellers in a practical data trading market,and this makes it challenging to maximize social *** solve these problems,this paper proposes a Social-Welfare-Oriented Many-to-Many Trading Mechanism(SOMTM),which integrates three entities,a trading process and an algorithm named Many-to-Many Trading Algorithm(MMTA).Based on the market scale,market dominated-side and market fixed-side,simulations verify the convergency,economic properties and efficiency of SOMTM.

关键词： Data trading Double auction Social welfare Game theory Smart city

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SGAN-RL: Fusion classification method for network intrusion detection based on data augmentation

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Journal of Intelligent and Fuzzy Systems 2025年第4期48卷 569-583页

作者： Li, Lining Liu, Shuai Ren, Rong Wu, Lianhai Wang, Qian Zhang, Bing College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Information Science and Engineering Yanshan University P.R. Qinhuangdao China The Key Laboratory of Software Engineering Yanshan University Qinhuangdao China

Although traditional research methods for intrusion detection can effectively prevent and mitigate issues such as data leaks to avoid severe consequences, existing intrusion detection technologies encounter limitations, including low classification accuracy and high false positive rates, particularly when dealing with high-dimensional, complex network anomaly traffic data. Additionally, the common problem of class imbalance in intrusion detection datasets exacerbates these challenges. This paper introduces a novel approach to network intrusion detection classification, termed SGAN-RL, which integrates ResNet and LSTM models based on the semi-supervised generative adversarial network (SGAN) framework. This method utilizes SGAN to generate synthetic data resembling real intrusion detection data and continually trains the discriminator to produce high-quality data, thereby enhancing data distribution balance and quality. Moreover, a fusion intrusion detection classification model is presented, leveraging the strengths of ResNet and LSTM architectures to capture spatial and temporal features, respectively. This synergistic fusion enhances the model’s capacity for comprehensive data representation, resulting in improved performance in intrusion detection data classification tasks. Experiments conducted on intrusion detection datasets NSL-KDD, UNSW-NB15, and CIC-IDS2017 demonstrate that the proposed model enhances accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC, respectively, showcasing its robust performance and reliability in network intrusion detection classification. © 2025 – IOS Press. All rights reserved.

关键词： Network intrusion

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