Robust and Discriminative Feature Learning via Mutual Information Maximization for Object Detection in Aerial Images

Object detection in unmanned aerial vehicle(UAV)aerial images has become increasingly important in military and civil applications.General object detection models are not robust enough against interclass similarity and intraclass variability of small objects,and UAV-specific nuisances such as uncontrolledweather conditions.Unlike previous approaches focusing on high-level semantic information,we report the importance of underlying features to improve detection accuracy and robustness fromthe information-theoretic perspective.Specifically,we propose a robust and discriminative feature learning approach through mutual information maximization(RD-MIM),which can be integrated into numerous object detection methods for aerial images.Firstly,we present the rank sample mining method to reduce underlying feature differences between the natural image domain and the aerial image domain.Then,we design a momentum contrast learning strategy to make object features similar to the same category and dissimilar to different categories.Finally,we construct a transformer-based global attention mechanism to boost object location semantics by leveraging the high interrelation of different receptive fields.We conduct extensive experiments on the VisDrone and Unmanned Aerial Vehicle Benchmark Object Detection and Tracking(UAVDT)datasets to prove the effectiveness of the proposed method.The experimental results show that our approach brings considerable robustness gains to basic detectors and advanced detection methods,achieving relative growth rates of 51.0%and 39.4%in corruption robustness,respectively.Our code is available at https://github.com/cq100/RD-MIM(accessed on 2 August 2024).

The Fusion of Temporal Sequence with Scene Priori Information in Deep Learning Object Recognition

For some important object recognition applications such as intelligent robots and unmanned driving, images are collected on a consecutive basis and associated among themselves, besides, the scenes have steady prior features. Yet existing technologies do not take full advantage of this information. In order to take object recognition further than existing algorithms in the above application, an object recognition method that fuses temporal sequence with scene priori information is proposed. This method first employs YOLOv3 as the basic algorithm to recognize objects in single-frame images, then the DeepSort algorithm to establish association among potential objects recognized in images of different moments, and finally the confidence fusion method and temporal boundary processing method designed herein to fuse, at the decision level, temporal sequence information with scene priori information. Experiments using public datasets and self-built industrial scene datasets show that due to the expansion of information sources, the quality of single-frame images has less impact on the recognition results, whereby the object recognition is greatly improved. It is presented herein as a widely applicable framework for the fusion of information under multiple classes. All the object recognition algorithms that output object class, location information and recognition confidence at the same time can be integrated into this information fusion framework to improve performance.

Research on Heterogeneous Information Network Link Prediction Based on Representation Learning

A heterogeneous information network,which is composed of various types of nodes and edges,has a complex structure and rich information content,and is widely used in social networks,academic networks,e-commerce,and other fields.Link prediction,as a key task to reveal the unobserved relationships in the network,is of great significance in heterogeneous information networks.This paper reviews the application of presentation-based learning methods in link prediction of heterogeneous information networks.This paper introduces the basic concepts of heterogeneous information networks,and the theoretical basis of representation learning,and discusses the specific application of the deep learning model in node embedding learning and link prediction in detail.The effectiveness and superiority of these methods on multiple real data sets are demonstrated by experimental verification.

Data-Driven Learning Control Algorithms for Unachievable Tracking Problems

For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.

CoLM^(2)S:Contrastive self‐supervised learning on attributed multiplex graph network with multi‐scale information

Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines.

An Analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion

The learning status of learners directly affects the quality of learning.Compared with offline teachers,it is difficult for online teachers to capture the learning status of students in the whole class,and it is even more difficult to continue to pay attention to studentswhile teaching.Therefore,this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion.Specifically,a facial expression recognition model and an eye state recognition model are constructed to detect students’emotions and fatigue,respectively.By integrating the detected data with the homework test score data after online learning,an analysis model of students’online learning status is constructed.According to the PAD model,the learning state is expressed as three dimensions of students’understanding,engagement and interest,and then analyzed from multiple perspectives.Finally,the proposed model is applied to actual teaching,and procedural analysis of 5 different types of online classroom learners is carried out,and the validity of the model is verified by comparing with the results of the manual analysis.

Power Information System Database Cache Model Based on Deep Machine Learning

At present,the database cache model of power information system has problems such as slow running speed and low database hit rate.To this end,this paper proposes a database cache model for power information systems based on deep machine learning.The caching model includes program caching,Structured Query Language(SQL)preprocessing,and core caching modules.Among them,the method to improve the efficiency of the statement is to adjust operations such as multi-table joins and replacement keywords in the SQL optimizer.Build predictive models using boosted regression trees in the core caching module.Generate a series of regression tree models using machine learning algorithms.Analyze the resource occupancy rate in the power information system to dynamically adjust the voting selection of the regression tree.At the same time,the voting threshold of the prediction model is dynamically adjusted.By analogy,the cache model is re-initialized.The experimental results show that the model has a good cache hit rate and cache efficiency,and can improve the data cache performance of the power information system.It has a high hit rate and short delay time,and always maintains a good hit rate even under different computer memory;at the same time,it only occupies less space and less CPU during actual operation,which is beneficial to power The information system operates efficiently and quickly.

LC-NPLA: Label and Community Information-Based Network Presentation Learning Algorithm

Many network presentation learning algorithms(NPLA)have originated from the process of the random walk between nodes in recent years.Despite these algorithms can obtain great embedding results,there may be also some limitations.For instance,only the structural information of nodes is considered when these kinds of algorithms are constructed.Aiming at this issue,a label and community information-based network presentation learning algorithm(LC-NPLA)is proposed in this paper.First of all,by using the community information and the label information of nodes,the first-order neighbors of nodes are reconstructed.In the next,the random walk strategy is improved by integrating the degree information and label information of nodes.Then,the node sequence obtained from random walk sampling is transformed into the node representation vector by the Skip-Gram model.At last,the experimental results on ten real-world networks demonstrate that the proposed algorithm has great advantages in the label classification,network reconstruction and link prediction tasks,compared with three benchmark algorithms.

Deep learning in extracting tropical cyclone intensity and wind radius information from satellite infrared images—A review

热带气旋(TC)严重危害人类生命和财产安全,TC的实时监测一直是研究热点,随着空间和传感器技术的发展,卫星遥感已成为监测TC的主要手段.此外,深度学习具有卓越的数据挖掘能力,在地球科学中的表现优于基于物理或统计的算法,越来越多的深度学习算法被开发和应用于TC信息的提取,本文系统地回顾了深度学习在TC信息提取中的应用,并给出了深度学习模型在TC强度和风圈半径提取中的应用.此外,本文还展望了深度学习在TC信息提取中的应用前景.

基于STL-Informer-BiLSTM-XGB模型的供热负荷预测

供热负荷预测是指导供热系统调控的重要手段。提高供热负荷预测精度十分重要,针对机器学习中输出目标的分解预测,提出了一种基于季节和趋势分解(seasonal and trend decomposition using loess,STL)的供热负荷预测方法,构建了适用于供热负荷预测的输出目标。首先利用STL算法将供热负荷时间序列数据分解为趋势分量、周期分量和残差分量,分别训练Informer、BiLSTM和XGB模型,将构建好的3个分量预测模型的输出叠加作为初步预测结果,分析误差序列,以BiLSTM预测误差提高模型精度,构建出STL-Informer-BiLSTM-XGB预测模型。将上述模型与常用预测模型进行对比,结果表明所构建的STL-Informer-BiLSTM-XGB模型的MAPE、MAE和MSE分别为0.871%、96.18和13202.2,预测效果最优,验证了所提出的方法具有较高的供热负荷预测精度。

Effects of Media and Distributed Information on Collaborative Concept-Learning

The present study explores the effects of media and distributed information on the performance of remotely located pairs of people′s completing a concept-learning task. Sixty pairs performed a concept-learning task using either audio-only or audio-plus-video for communication. The distribution of information includes three levels: with totally same information, with partly same information, and with totally different information. The subjects′ primary psychological functions were also considered in this study. The results showed a significant main effect of the amount of information shared by the subjects on the number of the negative instances selected by the subjects, and a significant main effect of media on the time taken by the subjects to complete the task.

MINDTL： Multiple Incomplete Domains Transfer Learning for Information Recommendation

Collaborative filtering is the most popular and successful information recommendation technique. However, it can suffer from data sparsity issue in cases where the systems do not have sufficient domain information. Transfer learning, which enables information to be transferred from source domains to target domain, presents an unprecedented opportunity to alleviate this issue. A few recent works focus on transferring user-item rating information from a dense domain to a sparse target domain, while almost all methods need that each rating matrix in source domain to be extracted should be complete. To address this issue, in this paper we propose a novel multiple incomplete domains transfer learning model for cross-domain collaborative filtering. The transfer learning process consists of two steps. First, the user-item ratings information in incomplete source domains are compressed into multiple informative compact cluster-level matrixes, which are referred as codebooks. Second, we reconstruct the target matrix based on the codebooks. Specifically, for the purpose of maximizing the knowledge transfer, we design a new algorithm to learn the rating knowledge efficiently from multiple incomplete domains. Extensive experiments on real datasets demonstrate that our proposed approach significantly outperforms existing methods.

Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning

The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning （DTCLE） was proposed. First, linear features （roads and ridges etc.） were excluded based on Deep Convolutional Neural Network （DCNN）. Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and eCognifion for cultivated land information extraction （ECLE）. The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3 （of extracting cultivated land） with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 9o.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.

Iterative Learning Control With Incomplete Information： A Survey

Abstract--This paper conducts a survey on iterative learn- ing control （ILC） with incomplete information and associated control system design, which is a frontier of the ILC field. The incomplete information, including passive and active types, can cause data loss or fragment due to various factors. Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection, storage, transmission, and processing, such as data dropouts, delays, disordering, and limited transmission bandwidth. Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied, such as sampling and quantization. This survey emphasizes two aspects： the first one is how to guarantee good learning performance and tracking performance with passive incomplete data, and the second is how to balance the control performance index and data demand by active means. The promising research directions along this topic are also addressed, where data robustness is highly emphasized. This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance, quantitatively, and promote further developments of ILC theory. Index Terms--Data dropout, data robustness, incomplete in- formation, iterative learning controi（ILC）, quantized control, sampled control, varying lengths.

Physics informed machine learning: Seismic wave equation

Similar to many fields of sciences,recent deep learning advances have been applied extensively in geosciences for both small-and large-scale problems.However,the necessity of using large training data and the’black box’nature of learning have limited them in practice and difficult to interpret.Furthermore,including the governing equations and physical facts in such methods is also another challenge,which entails either ignoring the physics or simplifying them using unrealistic data.To address such issues,physics informed machine learning methods have been developed which can integrate the governing physics law into the learning process.In this work,a 1-dimensional(1 D)time-dependent seismic wave equation is considered and solved using two methods,namely Gaussian process(GP)and physics informed neural networks.We show that these meshless methods are trained by smaller amount of data and can predict the solution of the equation with even high accuracy.They are also capable of inverting any parameter involved in the governing equation such as wave velocity in our case.Results show that the GP can predict the solution of the seismic wave equation with a lower level of error,while our developed neural network is more accurate for velocity(P-and S-wave)and density inversion.

Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System

The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so on.If an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.Awidespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.

Multi-agent reinforcement learning for edge information sharing in vehicular networks

To guarantee the heterogeneous delay requirements of the diverse vehicular services,it is necessary to design a full cooperative policy for both Vehicle to Infrastructure(V2I)and Vehicle to Vehicle(V2V)links.This paper investigates the reduction of the delay in edge information sharing for V2V links while satisfying the delay requirements of the V2I links.Specifically,a mean delay minimization problem and a maximum individual delay minimization problem are formulated to improve the global network performance and ensure the fairness of a single user,respectively.A multi-agent reinforcement learning framework is designed to solve these two problems,where a new reward function is proposed to evaluate the utilities of the two optimization objectives in a unified framework.Thereafter,a proximal policy optimization approach is proposed to enable each V2V user to learn its policy using the shared global network reward.The effectiveness of the proposed approach is finally validated by comparing the obtained results with those of the other baseline approaches through extensive simulation experiments.

Detection of Learner’s Concentration in Distance Learning System with Multiple Biological Information

The trend of distance learning education has increased year by year because of the rapid advancement of information and communication technologies. Distance learning system can be regarded as one of ubiquitous computing applications since the learners can study anywhere even in mobile environments. However, the instructor cannot know if the learners comprehend the lecture or not since each learner is physically isolated. Therefore, a framework which detects the learners’ concentration condition is required. If a distance learning system obtains the information that many learners are not concentrated on the class due to the incomprehensible lecture style, the instructor can perceive it through the system and change the presentation strategy. This is a context-aware technology which is widely used for ubiquitous computing services. In this paper, an efficient distance learning system, which accurately detects learners’ concentration condition during a class, is proposed. The proposed system uses multiple biological information which are learners’ eye movement metrics, i.e. fixation counts, fixation rate, fixation duration and average saccade length obtained by an eye tracking system. The learners’ concentration condition is classified by using machine learning techniques. The proposed system has performed the detection accuracy of 90.7% when Multilayer Perceptron is used as a classifier. In addition, the effectiveness of the proposed eye metrics has been confirmed. Furthermore, it has been clarified that the fixation duration is the most important eye metric among the four metrics based on the investigation of evaluation experiment.

Automatic Diagnosis of Polycystic Ovarian Syndrome Using Wrapper Methodology with Deep Learning Techniques

One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of recovery.Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition.This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies.Additionally,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers.In this research,the tri-stage wrapper method is used because it reduces the computation time.The proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this scenario.To start,this research pre-processed the information.Next for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is used.Then,various classification methods are tested and trained.Deep learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for categorization.The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms.

Mediating effect of e-learning quality on learning outcomes through student satisfaction in nursing education

Objective:This study aimed to determine the relationships between e-learning quality,student satisfaction,and learning outcomes and the mediating effect of student satisfaction.Methods:A cross-sectional quantitative correlational study using a predictive design and multivariate analysis method was employed in this study.A sample of 241 nursing students were recruited through an online survey based on a stratified random sampling technique.The variance-based Par tial Least Squares Structural Modeling analysis method was used to test the possible relationship and mediating effect among the variables.Results:The findings revealed statistical y significant relationships between e-learning quality,student satisfaction,and learning outcomes.A mediating effect of 37.2%is predicted for e-learning quality on learning outcomes through student satisfaction.Conclusions:This study emphasizes the learning needs of working nurses and the impact on their learning outcomes in e-learning nursing undergraduate programs.Advances in e-learning education have assisted nurses to be more self-sufficient in their pursuit of lifelong learning.