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Adaptively Enhancing Facial Expression Crucial Regions via a Local Non-local Joint Network

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Machine intelligence Research 2024年第2期21卷 331-348页

作者： Guanghui Shi Shasha Mao Shuiping Gou Dandan Yan Licheng Jiao Lin Xiong School of artificial intelligence Xidian UniversityXi'an 710000China applied Research Laboratory Sense TimeXi'an 710000China

Facial expression recognition(FER)is still challenging due to the small interclass discrepancy in facial expression data.In view of the significance of facial crucial regions for FER,many existing studies utilize the prior information from some annotated crucial points to improve the performance of FER.However,it is complicated and time-consuming to manually annotate facial crucial points,especially for vast wild expression images.Based on this,a local non-local joint network is proposed to adaptively enhance the facial crucial regions in feature learning of FER in this paper.In the proposed method,two parts are constructed based on facial local and non-local information,where an ensemble of multiple local networks is proposed to extract local features corresponding to multiple facial local regions and a non-local attention network is addressed to explore the significance of each local region.In particular,the attention weights obtained by the non-local network are fed into the local part to achieve interactive feedback between the facial global and local information.Interestingly,the non-local weights corresponding to local regions are gradually updated and higher weights are given to more crucial regions.Moreover,U-Net is employed to extract the integrated features of deep semantic information and low hierarchical detail information of expression images.Finally,experimental results illustrate that the proposed method achieves more competitive performance than several state-of-the-art methods on five benchmark datasets.

关键词： Facial expression recognition deep neural network multiple network ensemble attention network facial crucial regions

Machine learning for predicting the outcome of terminal ballistics events

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Defence Technology（防务技术） 2024年第1期31卷 14-26页

作者： Shannon Ryan Neeraj Mohan Sushma Arun Kumar AV Julian Berk Tahrima Hashem Santu Rana Svetha Venkatesh applied artificial intelligence Institute(A2I2) Deakin University75 Pigdons RdWaurn PondsVIC3216Australia

Machine learning(ML) is well suited for the prediction of high-complexity,high-dimensional problems such as those encountered in terminal ballistics.We evaluate the performance of four popular ML-based regression models,extreme gradient boosting(XGBoost),artificial neural network(ANN),support vector regression(SVR),and Gaussian process regression(GP),on two common terminal ballistics’ problems:(a)predicting the V50ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments,and(b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness.To achieve this we utilise two datasets,each consisting of approximately 1000samples,collated from public release sources.We demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range.Although extrapolation is not advisable for ML-based regression models,for applications such as lethality/survivability analysis,such capability is required.To circumvent this,we implement expert knowledge and physics-based models via enforced monotonicity,as a Gaussian prior mean,and through a modified loss function.The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models,providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not.The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types,target materials and thicknesses,and impact conditions significantly more diverse than that achievable from any existing analytical approach.Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster.We provide some general guidelines throughout for the development,application,and reporting of ML models in terminal ballistics problems.

关键词： Machine learning artificial intelligence Physics-informed machine learning Terminal ballistics Armour

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Adaptive 5G-and-beyond network-enabled interpretable federated learning enhanced by neuroevolution

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Science China(Information Sciences) 2024年第7期67卷 107-132页

作者： Bin CAO Jianwei ZHAO Xin LIU Yun LI State Key Laboratory of Reliability and intelligence of Electrical Equipment Hebei University of Technology School of artificial intelligence Hebei University of Technology School of Economics and Management Hebei University of Technology Industrial artificial intelligence centre Shenzhen Institute for Advanced StudyUniversity of Electronic Science and Technology of China

Mobile telemedicine systems based on the next-generation communication will significantly enhance deep fusion of network automation and federated learning(FL), but data privacy is a paramount issue in sectors like healthcare. This work hence considers FL augments 5G-and-beyond networks by training deep learning(DL) models without the need to exchange raw data. The substantial communication loads imposed on by extensive parameters involved in DL models are managed through adaptive scheduling mechanisms effectively. To address the opaque nature of DL models and to improve the interpretability of FL models, we introduce a convolutional fuzzy rough neural network specifically designed for medical image processing. We also develop a multiobjective memetic evolutionary algorithm to streamline and optimize the neural network architectures. Our comprehensive FL framework integrates smart scheduling, interpretable fuzzy rough logic,and neuroevolution. This framework is shown to improve communication efficiency, increase interpretability of diagnosis with protected privacy, and generate low-complexity neural architectures.

关键词： 5G-and-beyond network interpretable federated learning mobile telemedicine system fuzzy rough set theory neuroevolution

Emergence of machine language: towards symbolic intelligence with neural networks

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National Science Review 2024年第4期11卷 27-30页

作者： Yuqi Wang Xu-Yao Zhang Cheng-Lin Liu Tieniu Tan Zhaoxiang Zhang State Key Laboratory of Multimodal artificial intelligence Systems Institute of AutomationChinese Academy of Sciences

Representation learning is a core issue in artificial intelligence (AI). Currently,there exists a disparity in the choice of representation between humans and machines. Humans rely on discrete language for communicati...

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Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 37-57页

作者： Zhiqiang Pu Yi Pan Shijie Wang Boyin Liu Min Chen Hao Ma Yixiong Cui the School of artificial intelligence University of Chinese Academy of Sciences the Institute of Automation Chinese Academy of Sciences the Nanjing artificial intelligence Research of IA the School of Sports Engineering Beijing Sport University

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

关键词： artificial intelligence(AI) decision-making football review soccer sports analytics

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A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 593-631页

作者： Nilkanth Mukund Deshpande Shilpa Gite Biswajeet Pradhan Abdullah Alamri Chang-Wook Lee Department of Electronics&Telecommunication LavaleSymbiosis Institute of TechnologySymbiosis International(Deemed University)PuneMaharashtra412115India Electronics&Telecommunication Dr.Vithalrao Vikhe Patil College of EngineeringAhmednagarMaharashtra414111India artificial intelligence and Machine Learning Department Symbiosis Institute of TechnologySymbiosis International(Deemed)UniversityPune412115India symbiosis centre of applied AI(SCAAI) Symbiosis International(Deemed)UniversityPune412115India centre for AdvancedModelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering&ITUniversity of Technology SydneySydneyAustralia Earth Observation centre Institute of Climate ChangeUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Department of Geology&Geophysics College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia Department of Science Education Kangwon National UniversityChuncheon-si24341Korea

Infection of leukemia in humans causes many complications in its later stages.It impairs bone marrow’s ability to produce blood.Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case.The binary classification is employed to distinguish between normal and leukemiainfected cells.In addition,various subtypes of leukemia require different treatments.These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia.This entails using multi-class classification to determine the leukemia subtype.This is usually done using a microscopic examination of these blood cells.Due to the requirement of a trained pathologist,the decision process is critical,which leads to the development of an automated software framework for diagnosis.Researchers utilized state-of-the-art machine learning approaches,such as Support Vector Machine(SVM),Random Forest(RF),Na飗e Bayes,K-Nearest Neighbor(KNN),and others,to provide limited accuracies of classification.More advanced deep-learning methods are also utilized.Due to constrained dataset sizes,these approaches result in over-fitting,reducing their outstanding performances.This study introduces a deep learning-machine learning combined approach for leukemia diagnosis.It uses deep transfer learning frameworks to extract and classify features using state-of-the-artmachine learning classifiers.The transfer learning frameworks such as VGGNet,Xception,InceptionResV2,Densenet,and ResNet are employed as feature extractors.The extracted features are given to RF and XGBoost classifiers for the binary and multi-class classification of leukemia cells.For the experimentation,a very popular ALL-IDB dataset is used,approaching a maximum accuracy of 100%.A private real images dataset with three subclasses of leukemia images,including Acute Myloid Leukemia(AML),Chronic Lymphocytic Leukemia(CLL),and Chronic Myloid Leukemia(CML),is also employed to generalize the system.This dataset achieves an impressive multi-class classi

关键词： Leukemia diagnosis deep learning machine learning random forest XGBoost

A credibility-aware swarm-federated deep learning framework in internet of vehicles

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Digital Communications and Networks 2024年第1期10卷 150-157页

作者： Zhe Wang Xinhang Li Tianhao Wu Chen Xu Lin Zhang School of artificial intelligence Beijing University of Posts and TelecommunicationsBeijing100876China

Although Federated Deep Learning(FDL)enables distributed machine learning in the Internet of Vehicles(IoV),it requires multiple clients to upload model parameters,thus still existing unavoidable communication overhead and data privacy risks.The recently proposed Swarm Learning(SL)provides a decentralized machine learning approach for unit edge computing and blockchain-based coordination.A Swarm-Federated Deep Learning framework in the IoV system(IoV-SFDL)that integrates SL into the FDL framework is proposed in this paper.The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL,then aggregates the global FDL model among different SL groups with a credibility weights prediction algorithm.Extensive experimental results show that compared with the baseline frameworks,the proposed IoV-SFDL framework reduces the overhead of client-to-server communication by 16.72%,while the model performance improves by about 5.02%for the same training iterations.

关键词： Swarm learning Federated deep learning Internet of vehicles Privacy Efficiency

Efficient Unsupervised Image Stitching Using Attention Mechanism with Deep Homography Estimation

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Computers, Materials & Continua 2024年第4期79卷 1319-1334页

作者： Chunbin Qin Xiaotian Ran School of artificial intelligence Henan UniversityZhengzhou450000China

Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lackingunique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life scenesseverely undermines the reliability of supervised learning methods in image stitching. Furthermore, existing deeplearning architectures designed for image stitching are often too bulky to be deployed on mobile and peripheralcomputing devices. To address these challenges, this study proposes a novel unsupervised image stitching methodbased on the YOLOv8 (You Only Look Once version 8) framework that introduces deep homography networksand attentionmechanisms. Themethodology is partitioned into three distinct stages. The initial stage combines theattention mechanism with a pooling pyramid model to enhance the detection and recognition of compact objectsin images, the task of the deep homography networks module is to estimate the global homography of the inputimages consideringmultiple viewpoints. The second stage involves preliminary stitching of the masks generated inthe initial stage and further enhancement through weighted computation to eliminate common stitching artifacts.The final stage is characterized by adaptive reconstruction and careful refinement of the initial stitching results.Comprehensive experiments acrossmultiple datasets are executed tometiculously assess the proposed model. Ourmethod’s Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) improved by 10.6%and 6%. These experimental results confirm the efficacy and utility of the presented model in this paper.

关键词： Unsupervised image stitching deep homography estimation YOLOv8 attention mechanism

Spiking neural networks in intelligent control systems:a perspective

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Science China(Information Sciences) 2024年第7期67卷 324-326页

作者： Anguo ZHANG Yongduan SONG School of artificial intelligence Anhui University School of Automation Chongqing University

artificial intelligence (AI) has undergone substantial advancements in recent years, prompting an era of innovation in various domains. Among the groundbreaking contributions, neural networks stand at the forefront, powering applications ranging from computer vision and natural language processing to intelligent control systems [1, 2].

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