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Improving long-tail classification via decoupling and regularisation

CAAI Transactions on Intelligence technology 2025年第1期10卷 62-71页

作者： Shuzheng Gao Chaozheng Wang Cuiyun Gao Wenjian Luo Peiyi Han Qing Liao Guandong Xu School of Computer Science and Technology Harbin Institute of TechnologyShenzhenChina Peng Cheng Laboratory ShenzhenChina Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Harbin Institute of TechnologyShenzhenChina Faculty of Engineering and Information Technology University of Technology SydneySydneyNew South WalesAustralia

Real-world data always exhibit an imbalanced and long-tailed distribution,which leads to poor performance for neural network-based *** methods mainly tackle this problem by reweighting the loss function or rebalancing the ***,one crucial aspect overlooked by previous research studies is the imbalanced feature space problem caused by the imbalanced angle *** this paper,the authors shed light on the significance of the angle distribution in achieving a balanced feature space,which is essential for improving model performance under long-tailed ***,it is challenging to effectively balance both the classifier norms and angle distribution due to problems such as the low feature *** tackle these challenges,the authors first thoroughly analyse the classifier and feature space by decoupling the classification logits into three key components:classifier norm(*** magnitude of the classifier vector),feature norm(*** magnitude of the feature vector),and cosine similarity between the classifier vector and feature *** this way,the authors analyse the change of each component in the training process and reveal three critical problems that should be solved,that is,the imbalanced angle distribution,the lack of feature discrimination,and the low feature *** from this analysis,the authors propose a novel loss function that incorporates hyperspherical uniformity,additive angular margin,and feature norm *** component of the loss function addresses a specific problem and synergistically contributes to achieving a balanced classifier and feature *** authors conduct extensive experiments on three popular benchmark datasets including CIFAR-10/100-LT,ImageNet-LT,and iNaturalist *** experimental results demonstrate that the authors’loss function outperforms several previous state-of-the-art methods in addressing the challenges posed by imbalanced and longtailed datasets,t

关键词： computer vision image classification long-tailed data machine learning

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Recent Developments in Authentication Schemes Used in Machine-Type Communication Devices in Machine-to-Machine Communication: Issues and Challenges

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Computers, Materials & Continua 2024年第4期79卷 93-115页

作者： Shafi Ullah Sibghat Ullah Bazai Mohammad Imran Qazi Mudassar Ilyas Abid Mehmood Muhammad Asim Saleem Muhmmad Aasim Rafique Arsalan Haider Ilyas Khan Sajid Iqbal Yonis Gulzar Kauser Hameed Department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Department of Information Technology Balochistan University of Information TechnologyEngineeringand Management SciencesQuetta87300Pakistan Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityHofuf in Al-Ahsa31982Saudi Arabia Department of Software Engineering College of ComputingRiphah International UniversityFaisalabad44000Pakistan Department of Electrical Engineering Balochistan University of Information Technology Engineeringand Management SciencesQuetta87300Pakistan Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices(MTCDs) regularly share extensive data without human intervention while making all types of decisions. Thesedecisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeatmonitoring, and several different alert systems. Many of these devices simultaneously share data to form anautomated system. The data shared between machine-type communication devices (MTCDs) is prone to risk dueto limited computational power, internal memory, and energy capacity. Therefore, securing the data and devicesbecomes challenging due to factors such as dynamic operational environments, remoteness, harsh conditions,and areas where human physical access is difficult. One of the crucial parts of securing MTCDs and data isauthentication, where each devicemust be verified before data transmission. SeveralM2Mauthentication schemeshave been proposed in the literature, however, the literature lacks a comprehensive overview of current M2Mauthentication techniques and the challenges associated with them. To utilize a suitable authentication schemefor specific scenarios, it is important to understand the challenges associated with it. Therefore, this article fillsthis gap by reviewing the state-of-the-art research on authentication schemes in MTCDs specifically concerningapplication categories, security provisions, and performance efficiency.

关键词： Authentication cyber security internet of things machine-type communication devices machine-to-machine communication

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A Novel Trajectory Planning Method Based on Trust Region Policy Optimization

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Ye, Bao-Lin Zhang, Jiajie Li, Lingxi Wu, Weimin Jiaxing Key Laboratory of Smart Transportations the School of Information Science and Engineering Jiaxing University China Elmore Family School of Electrical and Computer Engineering Purdue University Indianapolis USA State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Trajectory planning method is a research hotspot in autonomous driving. Existing reinforcement learning-based trajectory planning methods suffer from unstable performance due to the strong randomness of network weight parameter updates during the training process. Therefore, this paper proposes a novel trajectory planning method based on deep reinforcement learning trust region policy optimization (TRPO). Firstly, in order to enhance the robustness of the trajectory planning method based on deep reinforcement learning TRPO, a TRPO-LSTM based decision model was proposed. More specifically, a long short term memory (LSTM) based state feature extraction network was designed and embeded into a TRPO-based decision model to enhance the ability of TRPO to extract information from the environmental state space. Secondly, in order to make the planned trajectory adaptive to the dynamic changes of traffic environment, we presented a novel TRPO-LSTM trajectory fitting algorithm. To the best of our knowledge, this is the first work aiming at applying the TRPO-LSTM based decision model in the trajectory fitting process to search the optimal longitudinal trajectory speed. Finally, the proposed trajectory planning method was implemented and simulated on the CARLA simulator. The experimental results show that, compared with existing trajectory planning methods based on deep reinforcement learning algorithms, our proposed method achieves a cumulative reward improvement of over 28.9% in the scenario of four lane highway, and has better robustness. Meanwhile, the proposed method can achieve a lower collision rate of 0.93% while improving the average speed and comfort of vehicle driving. IEEE

关键词： Heuristic algorithms

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Mitigating the negative catalytic effect of CuO by FAS-17 coated Al nanopowder:Isothermal ageing of Al/CuO nanothermite at 71°C and 60%relative humidity

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Defence technology（防务技术） 2024年第4期34卷 156-167页

作者： Fuwei Li Qian Wang Jian Cheng Zehua Zhang Yuxuan Zhou Keer Ouyang Jianbing Xu Yinghua Ye Ruiqi Shen School of Chemistry and Chemical Engineering Nanjing University of Science&TechnologyNanjing 210094China Micro-Nano Energetic Devices Key Laboratory Ministry of Industry and Information TechnologyNanjing 210094JiangsuChina Institute of Space Propulsion Nanjing University of Science and TechnologyNanjing 210094China

The safety and reliability of weapon systems would be significantly affected by changes in the performance of energetic materials due to ambient temperature and *** have promising applications due to their excellent *** it becomes extremely important to understand their aging and failure process in the environment before using ***,the aging and failure process of Al/CuO in 71°C/60%RH were investigated,and showed that CuO nanoparticles negatively catalyze Al nanopowders,resulting in rapid *** anti-aging effect of FAS-17-coated Al nanopowder was also *** aging process of Al,Al/CuO,and Al@FAS-17/CuO in high humidity and heat environment were revealed by quasi-in situ SEM and TEM *** with the aging of pure Al,the Al nanopowder in the nanothermites strongly agglomerated with the CuO nanopowder and hydrated *** may be caused by CuO catalyzed hydration of Al *** energy release experiments showed that the performance of Al/CuO decreased rapidly and failed to ignite after 4 h of *** contrast,the Al@FAS-17/CuO thermite can achieve long-term stability of up to 60 h in the same environment by simple cladding of *** is found that FAS-17 coated Al nanopowder can prevent both particle agglomeration and water erosion,which is an effective means to make nanothermites application in high humidity and heat environment.

关键词： Al/CuO nanothermite FAS-17 coating Aging and failure process

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Photothermal catalytic C-C coupling to ethylene from CO_(2) with high efficiency by synergistic cooperation of oxygen vacancy and half-metallic WTe_(2)

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Journal of Energy Chemistry 2024年第6期93卷 547-556,I0014页

作者： Xiaoyue Zhang Yong Yang Yingjie Hu Lijun Xiong Tianyu Wang Panjie Li Jinyou Shen Key Laboratory of Environmental Remediation and Ecological Health Ministry of Industry and Information TechnologySchool of Environmental and Biological EngineeringNanjing University of Science and TechnologyNanjing 210094JiangsuChina School of Chemistry and Chemical Engineering Nanjing University of Science and TechnologyNanjing 210094JiangsuChina Nanjing Key Laboratory of Advanced Functional Materials Nanjing Xiaozhuang UniversityNanjing 210094JiangsuChina Jiangsu Environmental Engineering Technology Co.Ltd Nanjing 210019JiangsuChina

Photothermal catalytic CO_(2) conversion provides an effective solution targeting carbon neutrality by synergistic utilization of photon and ***,the C-C coupling initiated by photothermal catalysis is still a big ***,a three-dimensional(3D)hierarchical W_(18)O_(49)/WTe_(2) hollow nanosphere is constructed through in-situ embodying of oxygen vacancy and tellurium on the scaffold of WO_(3).The light absorption towards near-infrared spectral region and CO_(2) adsorption are enhanced by the formation of half-metal WTe_(2) and the unique hierarchical hollow *** with the generation of oxygen vacancy with strengthened CO_(2) capture,the photothermal effect on the samples can be sufficiently exploited for activating the CO_(2) *** particular,the close contact between W_(18)O_(49)and WTe_(2) largely promotes the photoinduced charge separation and mass transfer,and thus the~*CHO intermediate formation and fixedness are *** a result,the C-C coupling can be evoked between tungsten and tellurium atoms on WTe_(2).The ethylene production by optimized W_(18)O_(49)/WTe_(2) reaches 147.6μmol g^(-1)with the selectivity of 80%.The in-situ diffuse reflectance infrared Fourier transform spectroscopy(DRIFTS)and density functional theory(DFT)calculations are performed to unveil the presence and significance of aldehyde intermediate groups in C-C *** half-metallic WTe_(2) cocatalyst proposes a new approach for efficient CO_(2) conversion with solar energy,and may especially create a new platform for the generation of multi-carbon products.

关键词： Photothermal catalysis Half-metal WTe_(2) C–C coupling Carbon neutrality

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Analysis of community question-answering issues via machine learning and deep learning:State-of-the-art review

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CAAI Transactions on Intelligence technology 2023年第1期8卷 95-117页

作者： Pradeep Kumar Roy Sunil Saumya Jyoti Prakash Singh Snehasish Banerjee Adnan Gutub Department of Computer Science and Engineering Indian Institute of Information TechnologySuratIndia Indian Institute of Information Technology DharwadIndia Department of Computer Science and Engineering National Institute of TechnologyPatnaIndia The York Management School University of YorkYorkUK Computer Engineering Department College of Computer&Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic *** have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs *** clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature *** goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)*** final review included 133 *** research themes include question quality,answer quality,and expert *** terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack *** scope of most articles was confined to just one platform with few cross-platform *** with ML outnumber those with ***,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.

关键词： answer quality community question answering deep learning expert user machine learning question quality

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Advancing Gender, Age and Ethnicity with YOLOv5 and Transfer Learning 6th

Advancing Gender, Age and Ethnicity with YOLOv5 and Transfer...

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6th International Conference on information systems and Management science, ISMS 2023

作者： Bhattacharya, Varun Tripathy, Balakrushna School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9783031664090

The detection of gender, age, and ethnicity using machine learning techniques holds significant promise for applications in marketing, healthcare, and law enforcement. Previous methods relied on conventional machine learning and manual feature engineering, which often struggled to capture the complex relationships between facial features and these demographic factors. To overcome these limitations, our novel approach leverages the powerful YOLOv5 framework, integrating transfer learning and deep neural networks. This cutting-edge method excels at accurately classifying gender, age, and ethnicity from facial images, thanks to its deep neural network architecture with advanced object detection capabilities. Our work represents a substantial advancement in gender, age, and ethnicity prediction, showcasing the potential of advanced machine learning techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Effect of surface modification on the radiation stability of diamond ohmic contacts

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Chinese Physics B 2024年第2期33卷 444-448页

作者：牟恋希赵上熳王鹏原晓芦刘金龙朱志甫陈良贤魏俊俊欧阳晓平李成明 Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China School of Mechanical Engineering University of Science and Technology BeijingBeijing 100083China School of Information Engineering Zhengzhou University of TechnologyZhengzhou 450044China State Key Laboratory of Intense Pulsed Radiation Simulation and Effect Northwest Institute of Nuclear TechnologyXi’an 710024China

The ohmic contact interface between diamond and metal is essential for the application of diamond *** modification can significantly affect the contact performance and eliminate the interface polarization ***,the radiation stability of a diamond detector is also sensitive to surface *** this work,the influence of surface modification technology on a diamond ohmic contact under high-energy radiation was *** radiation,the specific contact resistivities(ρc)between Ti/Pt/Au-hydrogen-terminated diamond(H-diamond)and Ti/Pt/Au-oxygenterminated diamond(O-diamond)were 2.0×10^(-4)W·cm^(2) and 4.3×10^(-3)Wcm^(2),*** 10 MeV electron radiation,the ρc of Ti/Pt/Au H-diamond and Ti/Pt/Au O-diamond were 5.3×10^(-3)W·cm^(2)and 9.1×10^(-3)W·cm^(2),*** rates of change of ρc of H-diamond and O-diamond after radiation were 2550%and 112%,*** electron radiation promotes bond reconstruction of the diamond surface,resulting in an increase in ρc.

关键词： single crystal diamond ohmic contact surface modification electron radiation

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Advancing Plant Disease Prediction Accuracy with ViT Based Deep Learning Model

Advancing Plant Disease Prediction Accuracy with ViT Based D...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent systems and Communications, COSMIC 2024

作者： Sam, Felicita Kavitha, B.R. School of Computer Science Engineering and Information Systems Vellore Institute of Technology TamilNadu Vellore India

ISBN: (纸本)9798331517892

Agriculture is the key source for many people's livelihoods and an important contributor to a country's economy. There is a huge demand to streamline the process in the field of agriculture by integrating Computing into the traditional way of farming. Computing plays a key role in handling this sector with many newer revolutions that would automate the various processes involved in overall farming. Particularly, machine and deep learning based models have shown remarkable results in smart farming. Rice crop is considered in this work which is the major crop in India. It is necessary to meet the demands of the growing population in terms of sufficient production of this crop. One of the key issues in this crop is the plant diseases that impact the overall crop yield production considerably. Among the various diseases, the most commonly occurring disease is White stem Borer. Diagnosing the disease at an early stage could treat the problem at the earliest to ensure the appropriate amount of yield production. In this paper, a Vision transformer (ViT) based deep learning model is proposed to predict the white stem borer disease on the PlantDoc dataset and the results obtained are compared with the other promising CNN based pre trained models like VGG19, InceptionV3, Resnet50. The experimental output shows that Vision transformer based model outperforms the other proven models with an accuracy of 96%. © 2024 IEEE.

关键词： Plant diseases

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Embedded Topic Models Enhanced by Wikification 1

Embedded Topic Models Enhanced by Wikification

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1st Workshop on Advancing Natural Language Processing for Wikipedia, WikiNLP 2024

作者： Shibuya, Takashi Utsuro, Takehito Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Japan

ISBN: (纸本)9798891761889

Topic modeling analyzes a collection of documents to learn meaningful patterns of words. However, previous topic models consider only the spelling of words and do not take into consideration the homography of words. In this study, we incorporate the Wikipedia knowledge into a neural topic model to make it aware of named entities. We evaluate our method on two datasets, 1) news articles of New York Times and 2) the AIDA-CoNLL dataset. Our experiments show that our method improves the performance of neural topic models in generalizability. Moreover, we analyze frequent terms in each topic and the temporal dependencies between topics to demonstrate that our entity-aware topic models can capture the time-series development of topics well. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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