On Developing the Questionnaire for Evaluating PBL's Multiple Learning Achievements

How to comprehensively,scientifically,objectively and impartially evaluate the multiple learning achievements of the PBL model should be highlighted when PBL is introduced and applied.A questionnaire of a total of 23 items involving such dimensions of language proficiency,subject contents and 21 st Century skills was designed.Its reliability and validity were tested and well-met with the statistical requirements.

Convergence analysis for complementary-label learning with kernel ridge regression

Complementary-label learning(CLL)aims at finding a classifier via samples with complementary labels.Such data is considered to contain less information than ordinary-label samples.The transition matrix between the true label and the complementary label,and some loss functions have been developed to handle this problem.In this paper,we show that CLL can be transformed into ordinary classification under some mild conditions,which indicates that the complementary labels can supply enough information in most cases.As an example,an extensive misclassification error analysis was performed for the Kernel Ridge Regression(KRR)method applied to multiple complementary-label learning(MCLL),which demonstrates its superior performance compared to existing approaches.

Classification of hyperspectral remote sensing images based on simulated annealing genetic algorithm and multiple instance learning

A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.

PT-MIL:Parallel transformer based on multi-instance learning for osteoporosis detection in panoramic oral radiography

Osteoporosis is a systemic disease characterized by low bone mass,impaired bone microstruc-ture,increased bone fragility,and a higher risk of fractures.It commonly affects postmenopausal women and the elderly.Orthopantomography,also known as panoramic radiography,is a widely used imaging technique in dental examinations due to its low cost and easy accessibility.Previous studies have shown that the mandibular cortical index(MCI)derived from orthopantomography can serve as an important indicator of osteoporosis risk.To address this,this study proposes a parallel Transformer network based on multiple instance learning.By introducing parallel modules that alleviate optimization issues and integrating multiple-instance learning with the Transformer architecture,our model effectively extracts information from image patches.Our model achieves an accuracy of 86%and an AUC score of 0.963 on an osteoporosis dataset,which demonstrates its promising and competitive performance.

Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning

Numerous studies have demonstrated that human microRNAs(miRNAs)and diseases are associated and studies on the microRNA-disease association(MDA)have been conducted.We developed a model using a low-rank approximation-based link propagation algorithm with Hilbert–Schmidt independence criterion-based multiple kernel learning(HSIC-MKL)to solve the problem of the large time commitment and cost of traditional biological experiments involving miRNAs and diseases,and improve the model effect.We constructed three kernels in miRNA and disease space and conducted kernel fusion using HSIC-MKL.Link propagation uses matrix factorization and matrix approximation to effectively reduce computation and time costs.The results of the experiment show that the approach we proposed has a good effect,and,in some respects,exceeds what existing models can do.

Incremental support vector machine algorithm based on multi-kernel learning

A new incremental support vector machine （SVM） algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set learning problem can be solved effectively. Furthermore, different punishments are adopted in allusion to the training subset and the acquired support vectors, which may help to improve the performance of SVM. Simulation results indicate that the proposed algorithm can not only solve the model selection problem in SVM incremental learning, but also improve the classification or prediction precision.

A stacked multiple kernel support vector machine for blast inducedflyrock prediction

As a widely used rock excavation method in civil and mining construction works, the blasting operations and theinduced side effects are always investigated by the existing studies. The occurrence of flyrock is regarded as one ofthe most important issues induced by blasting operations, since the accurate prediction of which is crucial fordelineating safety zone. For this purpose, this study developed a flyrock prediction model based on 234 sets ofblasting data collected from Sugun Copper Mine site. A stacked multiple kernel support vector machine (stackedMK-SVM) model was proposed for flyrock prediction. The proposed stacked structure can effectively improve themodel performance by addressing the importance level of different features. For comparison purpose, 6 othermachine learning models were developed, including SVM, MK-SVM, Lagragian Twin SVM (LTSVM), ArtificialNeural Network (ANN), Random Forest (RF) and M5 Tree. This study implemented a 5-fold cross validationprocess for hyperparameters tuning purpose. According to the evaluation results, the proposed stacked MK-SVMmodel achieved the best overall performance, with RMSE of 1.73 and 1.74, MAE of 0.58 and 1.08, VAF of 98.95and 99.25 in training and testing phase, respectively.

Short-term Wind Power Prediction Based on Soft Margin Multiple Kernel Learning Method

For short-term wind power prediction,a soft margin multiple kernel learning(MKL)method is proposed.In order to improve the predictive effect of the MKL method for wind power,a kernel slack variable is introduced into each base kernel to solve the objective function.Two kinds of soft margin MKL methods based on hinge loss function and square hinge loss function can be obtained when hinge loss functions and square hinge loss functions are selected.The improved methods demonstrate good robustness and avoid the disadvantage of the hard margin MKL method which only selects a few base kernels and discards other useful kernels when solving the objective function,thereby achieving an effective yet sparse solution for the MKL method.In order to verify the effectiveness of the proposed method,the soft margin MKL method was applied to the second wind farm of Tianfeng from Xinjiang for short-term wind power single-step prediction,and the single-step and multi-step predictions of short-term wind power was also carried out using measured data provided by alberta electric system operator(AESO).Compared with the support vector machine(SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)methods as well as the SimpleMKL method under the same conditions,the experimental results demonstrate that the soft margin MKL method with different loss functions can efficiently achieve higher prediction accuracy and good generalization performance for short-term wind power prediction,which confirms the effectiveness of the method.

上海市级定点医院新型冠状病毒肺炎患者集中救治动态能力体系建设策略

2022年上海本轮疫情中,上海申康医院发展中心组成工作专班,通过发挥知识转移与组织动态能力组织传导策略,协同各类资源,搭建多元动态平台,提高市级医院应对内外部环境压力的能力,增强市级医院在疫情期间的救治体系以及救治能力。现总结在公共卫生应急状态下如何组织市级医院通过整合内在与外在资源转型成为集中救治定点医院,建立市级医院“一夜成军”的快速反应机制。

EEG-based Emotion Recognition Using Multiple Kernel Learning

Emotion recognition based on electroencephalography(EEG)has a wide range of applications and has great potential value,so it has received increasing attention from academia and industry in recent years.Meanwhile,multiple kernel learning(MKL)has also been favored by researchers for its data-driven convenience and high accuracy.However,there is little research on MKL in EEG-based emotion recognition.Therefore,this paper is dedicated to exploring the application of MKL methods in the field of EEG emotion recognition and promoting the application of MKL methods in EEG emotion recognition.Thus,we proposed a support vector machine(SVM)classifier based on the MKL algorithm EasyMKL to investigate the feasibility of MKL algorithms in EEG-based emotion recognition problems.We designed two data partition methods,random division to verify the validity of the MKL method and sequential division to simulate practical applications.Then,tri-categorization experiments were performed for neutral,negative and positive emotions based on a commonly used dataset,the Shanghai Jiao Tong University emotional EEG dataset(SEED).The average classification accuracies for random division and sequential division were 92.25%and 74.37%,respectively,which shows better classification performance than the traditional single kernel SVM.The final results show that the MKL method is obviously effective,and the application of MKL in EEG emotion recognition is worthy of further study.Through the analysis of the experimental results,we discovered that the simple mathematical operations of the features on the symmetrical electrodes could not effectively integrate the spatial information of the EEG signals to obtain better performance.It is also confirmed that higher frequency band information is more correlated with emotional state and contributes more to emotion recognition.In summary,this paper explores research on MKL methods in the field of EEG emotion recognition and provides a new way of thinking for EEG-based emotion recognition research.

An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency

An integrated and systematic database of sooting tendency with more than 190 kinds of fuels was obtained through a series of experimental investigations. The laser-induced incandescence (LII) method was used to acquire the 2D distribution of soot volume fraction, and an apparatus-independent yield sooting index (YSI) was experimentally obtained. Based on the database, a novel predicting model of YSI values for surrogate fuels was proposed with the application of a machine learning method, named the Bayesian multiple kernel learning (BMKL) model. A high correlation coefficient (0.986) between measured YSIs and predicted values with the BMKL model was obtained, indicating that the BMKL model had a reliable and accurate predictive capacity for YSI values of surrogate fuels. The BMKL model provides an accurate and low-cost approach to assess surrogate performances of diesel, jet fuel, and biodiesel in terms of sooting tendency. Particularly, this model is one of the first attempts to predict the sooting tendencies of surrogate fuels that concurrently contain hydrocarbon and oxygenated components and shows a satisfying matching level. During surrogate formulation, the BMKL model can be used to shrink the surrogate candidate list in terms of sooting tendency and ensure the optimal surrogate has a satisfying matching level of soot behaviors. Due to the high accuracy and resolution of YSI prediction, the BMKL model is also capable of providing distinguishing information of sooting tendency for surrogate design.

Multi-task multi-label multiple instance learning

For automatic object detection tasks,large amounts of training images are usually labeled to achieve more reliable training of the object classifiers;this is cost-expensive since it requires hiring professionals to label large-scale training images.When a large number of object classes come into view,the issue of obtaining a large enough amount of the labeled training images becomes more critical.There are three potential solutions to reduce the burden for image labeling:(1) allowing people to provide the object labels loosely at the image level rather than at the object level(e.g.,loosely-tagged images without identifying the exact object locations in the images) ;(2) harnessing large-scale collaboratively-tagged images that are available on the Internet;and,(3) developing new machine learning algorithms that can directly leverage large-scale collaboratively-or loosely-tagged images for achieving more eective training of a large number of object classifiers.Based on these observations,a multi-task multi-label multiple instance learning(MTML-MIL) algorithm is developed in this paper by leveraging both inter-object correlations and large-scale loosely-labeled images for object classifier training.By seamlessly integrating multi-task learning,multi-label learning,and multiple instance learning,our MTML-MIL algorithm can achieve more accurate training of a large number of inter-related object classifiers(where an object network is constructed for determining the inter-related learning tasks directly in the feature space rather than in the label space) .Our experimental results have shown that our MTML-MIL algorithm can achieve higher detection accuracy rates for automatic object detection.

An Adaptive Classifier Based Approach for Crowd Anomaly Detection

Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and security.Intelligent video surveillance systems make extensive use of data mining,machine learning and deep learning methods.In this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep learning.In this approach,Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded scenes.We use multiple instance learning(MIL)to dynamically develop a deep anomalous ranking framework.This technique predicts higher anomalous values for abnormal video frames by treating regular and irregular video bags and video sections.We use the multi-objective whale optimization algorithm to optimize the entire process and get the best results.The performance parameters such as accuracy,precision,recall,and F-score are considered to evaluate the proposed technique using the Python simulation tool.Our simulation results show that the proposed method performs better than the conventional methods on the public live video dataset.

An Efficient Multiple Predicate Learner

In this papers we examine the issue of learning multiple predicates from given training examples. A proposed MPL-CORE algorithm efficiently induces Horn clauses from examples and background knowledge by employing a single predicate learning module CORE. A fast failure mechanism is also proposed which contributes learning efficiency and learnability to the algorithm. MPL-CORE employs background knowledge that can be represented in intensional (Horn clauses) or extensional (ground atoms) forms during its learning process. With the fast failure mechanism, MPL-CORE outperforms previous multiple predicate learning systems in both the computational complexity and learnability.

Robust visual tracking via randomly projected instance learning

Purpose–The purpose of this paper is to design a robust tracking algorithm which is suitable for the real-time requirement and solves the mistake labeling issue in the appearance model of trackers with the spare features.Design/methodology/approach–This paper proposes a tracker to select the most discriminative randomly projected ferns and integrates a coarse-to-fine search strategy in this framework.First,the authors exploit multiple instance boosting learning to maximize the bag likelihood and select randomly projected fern from feature pool to degrade the effect of mistake labeling.Second,a coarse-to-fine search approach is first integrated into the framework of multiple instance learning(MIL)for less detections.Findings–The quantitative and qualitative experiments demonstrate that the tracker has shown favorable performance in efficiency and effective among the competitors of tracking algorithms.Originality/value–The proposed method selects the feature from the compressive domain by MIL AnyBoost and integrates the coarse-to-fine search strategy first to reduce the burden of detection.This paper designs a tracker with high speed and favorable results which is more suitable for real-time scene.

Feature Rescaling of Support Vector Machines

Support vector machines （SVMs） have widespread use in various classification problems. Although SVMs are often used as an off-the-shelf tool, there are still some important issues which require improvement such as feature rescaling. Standardization is the most commonly used feature rescaling method. However, standardization does not always improve classification accuracy. This paper describes two feature rescaling methods： multiple kernel learning-based rescaling （MKL-SVM） and kernel-target alignment-based rescaling （KTA-SVM）. MKL-SVM makes use of the framework of multiple kernel learning （MKL） and KTA-SVM is built upon the concept of kernel alignment, which measures the similarity between kernels. The proposed meth- ods were compared with three other methods： an SVM method without rescaling, an SVM method with standardization, and SCADSVM. Test results demonstrate that different rescaling methods apply to different situations and that the proposed methods outperform the others in general.

String kernels construction and fusion:a survey with bioinformatics application

The kernel method,especially the kernel-fusion method,is widely used in social networks,computer vision,bioinformatics,and other applications.It deals effectively with nonlinear classification problems,which can map linearly inseparable biological sequence data from low to high-dimensional space for more accurate differentiation,enabling the use of kernel methods to predict the structure and function of sequences.Therefore,the kernel method is significant in the solution of bioinformatics problems.Various kernels applied in bioinformatics are explained clearly,which can help readers to select proper kernels to distinguish tasks.Mass biological sequence data occur in practical applications.Research of the use of machine learning methods to obtain knowledge,and how to explore the structure and function of biological methods for theoretical prediction,have always been emphasized in bioinformatics.The kernel method has gradually become an important learning algorithm that is widely used in gene expression and biological sequence prediction.This review focuses on the requirements of classification tasks of biological sequence data.It studies kernel methods and optimization algorithms,including methods of constructing kernel matrices based on the characteristics of biological sequences and kernel fusion methods existing in a multiple kernel learning framework.

Robust Text Detection in Natural Scenes Using Text Geometry and Visual Appearance

This paper proposes a new two-phase approach to robust text detection by integrating the visual appearance and the geometric reasoning rules. In the first phase, geometric rules are used to achieve a higher recall rate. Specifically, a robust stroke width transform(RSWT) feature is proposed to better recover the stroke width by additionally considering the cross of two strokes and the continuousness of the letter border. In the second phase, a classification scheme based on visual appearance features is used to reject the false alarms while keeping the recall rate. To learn a better classifier from multiple visual appearance features, a novel classification method called double soft multiple kernel learning(DS-MKL) is proposed. DS-MKL is motivated by a novel kernel margin perspective for multiple kernel learning and can effectively suppress the influence of noisy base kernels. Comprehensive experiments on the benchmark ICDAR2005 competition dataset demonstrate the effectiveness of the proposed two-phase text detection approach over the state-of-the-art approaches by a performance gain up to 4.4% in terms of F-measure.