Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs

Graph learning,when used as a semi-supervised learning(SSL)method,performs well for classification tasks with a low label rate.We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi-or hyperspectral image segmentation.Our batch active learning approach selects a collection of unlabeled pixels that satisfy a graph local maximum constraint for the active learning acquisition function that determines the relative importance of each pixel to the classification.This work builds on recent advances in the design of novel active learning acquisition functions(e.g.,the Model Change approach in arXiv:2110.07739)while adding important further developments including patch-neighborhood image analysis and batch active learning methods to further increase the accuracy and greatly increase the computational efficiency of these methods.In addition to improvements in the accuracy,our approach can greatly reduce the number of labeled pixels needed to achieve the same level of the accuracy based on randomly selected labeled pixels.

Model Change Active Learning in Graph-Based Semi-supervised Learning

Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to best improve performance while limiting the number of new labels."Model Change"active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s).We pair this idea with graph-based semi-supervised learning(SSL)methods,that use the spectrum of the graph Laplacian matrix,which can be truncated to avoid prohibitively large computational and storage costs.We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution.We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.

Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm

AIM:To conduct a classification study of high myopic maculopathy(HMM)using limited datasets,including tessellated fundus,diffuse chorioretinal atrophy,patchy chorioretinal atrophy,and macular atrophy,and minimize annotation costs,and to optimize the ALFA-Mix active learning algorithm and apply it to HMM classification.METHODS:The optimized ALFA-Mix algorithm(ALFAMix+)was compared with five algorithms,including ALFA-Mix.Four models,including Res Net18,were established.Each algorithm was combined with four models for experiments on the HMM dataset.Each experiment consisted of 20 active learning rounds,with 100 images selected per round.The algorithm was evaluated by comparing the number of rounds in which ALFA-Mix+outperformed other algorithms.Finally,this study employed six models,including Efficient Former,to classify HMM.The best-performing model among these models was selected as the baseline model and combined with the ALFA-Mix+algorithm to achieve satisfactor y classification results with a small dataset.RESULTS:ALFA-Mix+outperforms other algorithms with an average superiority of 16.6,14.75,16.8,and 16.7 rounds in terms of accuracy,sensitivity,specificity,and Kappa value,respectively.This study conducted experiments on classifying HMM using several advanced deep learning models with a complete training set of 4252 images.The Efficient Former achieved the best results with an accuracy,sensitivity,specificity,and Kappa value of 0.8821,0.8334,0.9693,and 0.8339,respectively.Therefore,by combining ALFA-Mix+with Efficient Former,this study achieved results with an accuracy,sensitivity,specificity,and Kappa value of 0.8964,0.8643,0.9721,and 0.8537,respectively.CONCLUSION:The ALFA-Mix+algorithm reduces the required samples without compromising accuracy.Compared to other algorithms,ALFA-Mix+outperforms in more rounds of experiments.It effectively selects valuable samples compared to other algorithms.In HMM classification,combining ALFA-Mix+with Efficient Former enhances model performance,further demonstrating the effectiveness of ALFA-Mix+.

Active learning accelerated Monte-Carlo simulation based on the modified K-nearest neighbors algorithm and its application to reliability estimations

This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability.

Active Learning Strategies for Textual Dataset-Automatic Labelling

The Internet revolution has resulted in abundant data from various sources,including social media,traditional media,etcetera.Although the availability of data is no longer an issue,data labelling for exploiting it in supervised machine learning is still an expensive process and involves tedious human efforts.The overall purpose of this study is to propose a strategy to automatically label the unlabeled textual data with the support of active learning in combination with deep learning.More specifically,this study assesses the performance of different active learning strategies in automatic labelling of the textual dataset at sentence and document levels.To achieve this objective,different experiments have been performed on the publicly available dataset.In first set of experiments,we randomly choose a subset of instances from training dataset and train a deep neural network to assess performance on test set.In the second set of experiments,we replace the random selection with different active learning strategies to choose a subset of the training dataset to train the same model and reassess its performance on test set.The experimental results suggest that different active learning strategies yield performance improvement of 7% on document level datasets and 3%on sentence level datasets for auto labelling.

Enhancing Semantic Segmentation through Reinforced Active Learning: Combating Dataset Imbalances and Bolstering Annotation Efficiency

This research addresses the challenges of training large semantic segmentation models for image analysis,focusing on expediting the annotation process and mitigating imbalanced datasets.In the context of imbalanced datasets,biases related to age and gender in clinical contexts and skewed representation in natural images can affect model performance.Strategies to mitigate these biases are explored to enhance efficiency and accuracy in semantic segmentation analysis.An in-depth exploration of various reinforced active learning methodologies for image segmentation is conducted,optimizing precision and efficiency across diverse domains.The proposed framework integrates Dueling Deep Q-Networks(DQN),Prioritized Experience Replay,Noisy Networks,and Emphasizing Recent Experience.Extensive experimentation and evaluation of diverse datasets reveal both improvements and limitations associated with various approaches in terms of overall accuracy and efficiency.This research contributes to the expansion of reinforced active learning methodologies for image segmentation,paving the way for more sophisticated and precise segmentation algorithms across diverse domains.The findings emphasize the need for a careful balance between exploration and exploitation strategies in reinforcement learning for effective image segmentation.

Active learning based on maximizing information gain for content-based image retrieval

This paper describes a new method for active learning in content-based image retrieval. The proposed method firstly uses support vector machine (SVM) classifiers to learn an initial query concept. Then the proposed active learning scheme employs similarity measure to check the current version space and selects images with maximum expected information gain to solicit user's label. Finally, the learned query is refined based on the user's further feedback. With the combination of SVM classifier and similarity measure, the proposed method can alleviate model bias existing in each of them. Our experiments on several query concepts show that the proposed method can learn the user's query concept quickly and effectively only with several iterations.

Support Vector Machine active learning for 3D model retrieval

In this paper, we present a novel Support Vector Machine active learning algorithm for effective 3D model retrieval using the concept of relevance feedback. The proposed method learns from the most informative objects which are marked by the user, and then creates a boundary separating the relevant models from irrelevant ones. What it needs is only a small number of 3D models labelled by the user. It can grasp the user＇s semantic knowledge rapidly and accurately. Experimental results showed that the proposed algorithm significantly improves the retrieval effectiveness. Compared with four state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval performance after no more than two rounds of relevance feedback.

MII:A Novel Text Classification Model Combining Deep Active Learning with BERT

Active learning has been widely utilized to reduce the labeling cost of supervised learning.By selecting specific instances to train the model,the performance of the model was improved within limited steps.However,rare work paid attention to the effectiveness of active learning on it.In this paper,we proposed a deep active learning model with bidirectional encoder representations from transformers(BERT)for text classification.BERT takes advantage of the self-attention mechanism to integrate contextual information,which is beneficial to accelerate the convergence of training.As for the process of active learning,we design an instance selection strategy based on posterior probabilities Margin,Intra-correlation and Inter-correlation(MII).Selected instances are characterized by small margin,low intra-cohesion and high inter-cohesion.We conduct extensive experiments and analytics with our methods.The effect of learner is compared while the effect of sampling strategy and text classification is assessed from three real datasets.The results show that our method outperforms the baselines in terms of accuracy.

Tunnel face reliability analysis using active learning Kriging model——Case of a two-layer soils

This paper is devoted to the probabilistic stability analysis of a tunnel face excavated in a two-layer soil. The interface of the soil layers is assumed to be positioned above the tunnel roof. In the framework of limit analysis, a rotational failure mechanism is adopted to describe the face failure considering different shear strength parameters in the two layers. The surrogate Kriging model is introduced to replace the actual performance function to perform a Monte Carlo simulation. An active learning function is used to train the Kriging model which can ensure an efficient tunnel face failure probability prediction without loss of accuracy. The deterministic stability analysis is given to validate the proposed tunnel face failure model. Subsequently, the number of initial sampling points, the correlation coefficient, the distribution type and the coefficient of variability of random variables are discussed to show their influences on the failure probability. The proposed approach is an advisable alternative for the tunnel face stability assessment and can provide guidance for tunnel design.

Analyzing Cross-domain Transportation Big Data of New York City with Semi-supervised and Active Learning

The majority of big data analytics applied to transportation datasets suffer from being too domain-specific,that is,they draw conclusions for a dataset based on analytics on the same dataset.This makes models trained from one domain(e.g.taxi data)applies badly to a different domain(e.g.Uber data).To achieve accurate analyses on a new domain,substantial amounts of data must be available,which limits practical applications.To remedy this,we propose to use semi-supervised and active learning of big data to accomplish the domain adaptation task:Selectively choosing a small amount of datapoints from a new domain while achieving comparable performances to using all the datapoints.We choose the New York City(NYC)transportation data of taxi and Uber as our dataset,simulating different domains with 90%as the source data domain for training and the remaining 10%as the target data domain for evaluation.We propose semi-supervised and active learning strategies and apply it to the source domain for selecting datapoints.Experimental results show that our adaptation achieves a comparable performance of using all datapoints while using only a fraction of them,substantially reducing the amount of data required.Our approach has two major advantages:It can make accurate analytics and predictions when big datasets are not available,and even if big datasets are available,our approach chooses the most informative datapoints out of the dataset,making the process much more efficient without having to process huge amounts of data.

Adversarial Active Learning for Named Entity Recognition in Cybersecurity

Owing to the continuous barrage of cyber threats,there is a massive amount of cyber threat intelligence.However,a great deal of cyber threat intelligence come from textual sources.For analysis of cyber threat intelligence,many security analysts rely on cumbersome and time-consuming manual efforts.Cybersecurity knowledge graph plays a significant role in automatics analysis of cyber threat intelligence.As the foundation for constructing cybersecurity knowledge graph,named entity recognition(NER)is required for identifying critical threat-related elements from textual cyber threat intelligence.Recently,deep neural network-based models have attained very good results in NER.However,the performance of these models relies heavily on the amount of labeled data.Since labeled data in cybersecurity is scarce,in this paper,we propose an adversarial active learning framework to effectively select the informative samples for further annotation.In addition,leveraging the long short-term memory(LSTM)network and the bidirectional LSTM(BiLSTM)network,we propose a novel NER model by introducing a dynamic attention mechanism into the BiLSTM-LSTM encoderdecoder.With the selected informative samples annotated,the proposed NER model is retrained.As a result,the performance of the NER model is incrementally enhanced with low labeling cost.Experimental results show the effectiveness of the proposed method.

Novel Active Learning Method for Speech Recognition

In speech recognition, acoustic modeling always requires tremendous transcribed samples, and the transcription becomes intensively time-consuming and costly. In order to aid this labor-intensive process, Active Learning （AL） is adopted for speech recognition, where only the most informative training samples are selected for manual annotation. In this paper, we propose a novel active learning method for Chinese acoustic modeling, the methods for initial training set selection based on Kullback-Leibler Divergence （KLD） and sample evaluation based on multi-level confusion networks are proposed and adopted in our active learning system, respectively. Our experiments show that our proposed method can achieve satisfying performances.

Active Learning Improves Nursing Student Clinical Performance in an Academic Institution in Macao

Objective： To assess the outcome of the application of active learning during practicum among nursing students using clinical assessment and evaluation scores as a measurement. Methods： Nursing students were instructed on the basics of active learning prior to the initiation of their clinical experience. The participants were divided into 5groups of nursing students （ n = 56） across three levels （years 2-4） in a public academic institute of a bachelor degree program in Macao. Final clinical evaluation was averaged and compared between groups with and without intervention. Results： These nursing students were given higher appraisals in verbal and written comments than previous students without interventian. The groups with the invention achieved higher clinical assessment and evaluation scores on average than comparable groups without the active learning intervention. One group of sophomore nursing students （year 2） did not receive as high of evaluations as the other groups, receiving an average score of above 80. Conclusions＂ Nursing students must engage in active learning to demonstrate that they are willing to gain knowledge of theory, nursing skills and communication skills during the clinical practicum.

Implementing physically active learning:Future directions for research,policy,and practice

Purpose:To identify co-produced multi-stakeholder perspectives important for successful widespread physically active learning(PAL) adoption and implementation.Methods:A total of 35 stakeholders(policymakers n=9;commercial education sector,n=8;teachers,n=3;researchers,n=15) attended a design thinking PAL workshop.Participants formed 5 multi-disciplinary groups with at least 1 representative from each stakeholder group.Each group,facilitated by a researcher,undertook 2 tasks:(1) using Post-it Notes,the following question was answered:within the school day,what are the opportunities for learning combined with movement?and(2) structured as a washing-line task,the following question was answered:how can we establish PAL as the norm?All discussions were audio-recorded and transcribed.Inductive analyses were conducted by 4 authors.After the analyses were complete,the main themes and subthemes were assigned to 4 predetermined categories:(1) PAL design and implementation,(2) priorities for practice,(3) priorities for policy,and(4) priorities for research.Results:The following were the main themes for PAL implementation:opportunities for PAL within the school day,delivery environments,learning approaches,and the intensity of PAL.The main themes for the priorities for practice included teacher confidence and competence,resources to support delivery,and community of practice.The main themes for the policy for priorities included self-governance,the Office for Standards in Education,Children’s Services,and Skill,policy investment in initial teacher training,and curriculum reform.The main themes for the research priorities included establishing a strong evidence base,school-based PAL implementation,and a whole-systems approach.Conclusion:The present study is the first to identify PAL implementation factors using a combined multi-stakeholder perspective.To achieve wider PAL adoption and implementation,future interventions should be evidence based and address implementation factors at the classroom level(e.g.,approaches and delivery environments),school level(e.g.,communities of practice),and policy level(e.g.,initial teacher training).

Mining potential social relationship with active learning in LBSN

Rapid development of local-based social network(LBSN) makes it more convenient for researchers to carry out studies related to social network.Mining potential social relationship in LBSN is the most important one.Traditionally,researchers use topological relation of social network or telecommunication network to mine potential social relationship.But the effect is unsatisfactory as the network can not provide complete information of topological relation.In this work,a new model called PSRMAL is proposed for mining potential social relationships with LBSN.With the model,better performance is obtained and guaranteed,and experiments verify the effectiveness.

A Simple yet Effective Framework for Active Learning to Rank

While China has become the largest online market in the world with approximately 1 billion internet users,Baidu runs the world's largest Chinese search engine serving more than hundreds of millions of daily active users and responding to billions of queries per day.To handle the diverse query requests from users at the web-scale,Baidu has made tremendous efforts in understanding users'queries,retrieving relevant content from a pool of trillions of webpages,and ranking the most relevant webpages on the top of the res-ults.Among the components used in Baidu search,learning to rank(LTR)plays a critical role and we need to timely label an extremely large number of queries together with relevant webpages to train and update the online LTR models.To reduce the costs and time con-sumption of query/webpage labelling,we study the problem of active learning to rank(active LTR)that selects unlabeled queries for an-notation and training in this work.Specifically,we first investigate the criterion-Ranking entropy(RE)characterizing the entropy of relevant webpages under a query produced by a sequence of online LTR models updated by different checkpoints,using a query-by-com-mittee(QBC)method.Then,we explore a new criterion namely prediction variances(PV)that measures the variance of prediction res-ults for all relevant webpages under a query.Our empirical studies find that RE may favor low-frequency queries from the pool for la-belling while PV prioritizes high-frequency queries more.Finally,we combine these two complementary criteria as the sample selection strategies for active learning.Extensive experiments with comparisons to baseline algorithms show that the proposed approach could train LTR models to achieve higher discounted cumulative gain(i.e.,the relative improvement DCG4=1.38%)with the same budgeted labellingefforts.

Automatic Requirement Dependency Extraction Based on Integrated Active Learning Strategies

Since requirement dependency extraction is a cognitively challenging and error-prone task,this paper proposes an automatic requirement dependency extraction method based on integrated active learning strategies.In this paper,the coefficient of variation method was used to determine the corresponding weight of the impact factors from three different angles:uncertainty probability,text similarity difference degree and active learning variant prediction divergence degree.By combining the three factors with the proposed calculation formula to measure the information value of dependency pairs,the top K dependency pairs with the highest comprehensive evaluation value are selected as the optimal samples.As the optimal samples are continuously added into the initial training set,the performance of the active learning model using different dependency features for requirement dependency extraction is rapidly improved.Therefore,compared with other active learning strategies,a higher evaluation measure of requirement dependency extraction can be achieved by using the same number of samples.Finally,the proposed method using the PV-DM dependency feature improves the weight-F1 by 2.71%,the weight-recall by 2.45%,and the weight-precision by 2.64%in comparison with other strategies,saving approximately 46%of the labelled data compared with the machine learning approach.

An active learning workflow for predicting hydrogen atom adsorption energies on binary oxides based on local electronic transfer features

Machine learning combined with density functional theory(DFT)enables rapid exploration of catalyst descriptors space such as adsorption energy,facilitating rapid and effective catalyst screening.However,there is still a lack of models for predicting adsorption energies on oxides,due to the complexity of elemental species and the ambiguous coordination environment.This work proposes an active learning workflow(LeNN)founded on local electronic transfer features(e)and the principle of coordinate rotation invariance.By accurately characterizing the electron transfer to adsorption site atoms and their surrounding geometric structures,LeNN mitigates abrupt feature changes due to different element types and clarifies coordination environments.As a result,it enables the prediction of^(*)H adsorption energy on binary oxide surfaces with a mean absolute error(MAE)below 0.18 eV.Moreover,we incorporate local coverage(θ_(l))and leverage neutral network ensemble to establish an active learning workflow,attaining a prediction MAE below 0.2 eV for 5419 multi-^(*)H adsorption structures.These findings validate the universality and capability of the proposed features in predicting^(*)H adsorption energy on binary oxide surfaces.

A Facial Expression Recognition Method Integrating Uncertainty Estimation and Active Learning

The effectiveness of facial expression recognition(FER)algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression data.However,labeling large datasets demands significant human,time,and financial resources.Although active learning methods have mitigated the dependency on extensive labeled data,a cold-start problem persists in small to medium-sized expression recognition datasets.This issue arises because the initial labeled data often fails to represent the full spectrum of facial expression characteristics.This paper introduces an active learning approach that integrates uncertainty estimation,aiming to improve the precision of facial expression recognition regardless of dataset scale variations.The method is divided into two primary phases.First,the model undergoes self-supervised pre-training using contrastive learning and uncertainty estimation to bolster its feature extraction capabilities.Second,the model is fine-tuned using the prior knowledge obtained from the pre-training phase to significantly improve recognition accuracy.In the pretraining phase,the model employs contrastive learning to extract fundamental feature representations from the complete unlabeled dataset.These features are then weighted through a self-attention mechanism with rank regularization.Subsequently,data from the low-weighted set is relabeled to further refine the model’s feature extraction ability.The pre-trained model is then utilized in active learning to select and label information-rich samples more efficiently.Experimental results demonstrate that the proposed method significantly outperforms existing approaches,achieving an improvement in recognition accuracy of 5.09%and 3.82%over the best existing active learning methods,Margin,and Least Confidence methods,respectively,and a 1.61%improvement compared to the conventional segmented active learning method.