Variational data encoding and correlations in quantum-enhanced machine learning

Leveraging the extraordinary phenomena of quantum superposition and quantum correlation,quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical computers.This paper tackles two pivotal challenges in the realm of quantum computing:firstly,the development of an effective encoding protocol for translating classical data into quantum states,a critical step for any quantum computation.Different encoding strategies can significantly influence quantum computer performance.Secondly,we address the need to counteract the inevitable noise that can hinder quantum acceleration.Our primary contribution is the introduction of a novel variational data encoding method,grounded in quantum regression algorithm models.By adapting the learning concept from machine learning,we render data encoding a learnable process.This allowed us to study the role of quantum correlation in data encoding.Through numerical simulations of various regression tasks,we demonstrate the efficacy of our variational data encoding,particularly post-learning from instructional data.Moreover,we delve into the role of quantum correlation in enhancing task performance,especially in noisy environments.Our findings underscore the critical role of quantum correlation in not only bolstering performance but also in mitigating noise interference,thus advancing the frontier of quantum computing.

Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

A Study on the Correlations Between Chinese EFL Learners' Autonomous Learning and Their Cooperative Learning

Cooperative learning and learner autonomy have become heated topics in English teaching.Many researchers both at home and abroad have carried out the study on this field,and they proved that learner autonomy and cooperative learning are rel evant,cooperative learning can promote English majors’learning autonomy in terms of listening,reading,speaking,however,no obvious findings in vocabulary and writing.This study is conducted to prove the feasibilities and effectiveness of cooperative learning applied to foster first-year non-English majors’learning autonomy from the aspect of vocabulary and structure.The re sults of this study showed that cooperative learning was much more effective than traditional teaching approaches in fostering some positive affective factors.According to this research,cooperative learning can also improve learners’vocabulary and struc ture significantly.

Boosting Adaptive Weighted Broad Learning System for Multi-Label Learning

Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To address the above issues, we propose the multi-label weighted broad learning system(MLW-BLS) from the perspective of label imbalance weighting and label correlation mining. Further, we propose the multi-label adaptive weighted broad learning system(MLAW-BLS) to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set. Extensive experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.

Research on the Freezing Phenomenon of Quantum Correlation by Machine Learning

Quantum correlation shows a fascinating nature of quantum mechanics and plays an important role in some physics topics,especially in the field of quantum information.Quantum correlations of the composite system can be quantified by resorting to geometric or entropy methods,and all these quantification methods exhibit the peculiar freezing phenomenon.The challenge is to find the characteristics of the quantum states that generate the freezing phenomenon,rather than only study the conditions which generate this phenomenon under a certain quantum system.In essence,this is a classification problem.Machine learning has become an effective method for researchers to study classification and feature generation.In this work,we prove that the machine learning can solve the problem of X form quantum states,which is a problem of physical significance.Subsequently,we apply the density-based spatial clustering of applications with noise(DBSCAN)algorithm and the decision tree to divide quantum states into two different groups.Our goal is to classify the quantum correlations of quantum states into two classes:one is the quantum correlation with freezing phenomenon for both Rènyi discord(α=2)and the geometric discord(Bures distance),the other is the quantum correlation of non-freezing phenomenon.The results demonstrate that the machine learning method has reasonable performance in quantum correlation research.

Multi-Label Learning Based on Transfer Learning and Label Correlation

In recent years,multi-label learning has received a lot of attention.However,most of the existing methods only consider global label correlation or local label correlation.In fact,on the one hand,both global and local label correlations can appear in real-world situation at same time.On the other hand,we should not be limited to pairwise labels while ignoring the high-order label correlation.In this paper,we propose a novel and effective method called GLLCBN for multi-label learning.Firstly,we obtain the global label correlation by exploiting label semantic similarity.Then,we analyze the pairwise labels in the label space of the data set to acquire the local correlation.Next,we build the original version of the label dependency model by global and local label correlations.After that,we use graph theory,probability theory and Bayesian networks to eliminate redundant dependency structure in the initial version model,so as to get the optimal label dependent model.Finally,we obtain the feature extraction model by adjusting the Inception V3 model of convolution neural network and combine it with the GLLCBN model to achieve the multi-label learning.The experimental results show that our proposed model has better performance than other multi-label learning methods in performance evaluating.

Analysis of Negative Correlation Learning

This paper describes negative correlation learning for designing neural network ensembles. Negative correlation learning has been firstly analysed in terms of minimising mutual information on a regression task. By minimising the mutual information between variables extracted by two neural networks, they are forced to convey different information a-bout some features of their input. Based on the decision boundaries and correct response sets, negative correlation learning has been further studied on two pattern classification problems. The purpose of examining the decision boundaries and the correct response sets is not only to illustrate the learning behavior of negative correlation learning, but also to cast light on how to design more effective neural network ensembles. The experimental results showed the decision boundary of the trained neural network ensemble by negative correlation learning is almost as good as the optimum decision boundary.

Label correlation for partial label learning

Partial label learning aims to learn a multi-class classifier,where each training example corresponds to a set of candidate labels among which only one is correct.Most studies in the label space have only focused on the difference between candidate labels and non-candidate labels.So far,however,there has been little discussion about the label correlation in the partial label learning.This paper begins with a research on the label correlation,followed by the establishment of a unified framework that integrates the label correlation,the adaptive graph,and the semantic difference maximization criterion.This work generates fresh insight into the acquisition of the learning information from the label space.Specifically,the label correlation is calculated from the candidate label set and is utilized to obtain the similarity of each pair of instances in the label space.After that,the labeling confidence for each instance is updated by the smoothness assumption that two instances should be similar outputs in the label space if they are close in the feature space.At last,an effective optimization program is utilized to solve the unified framework.Extensive experiments on artificial and real-world data sets indicate the superiority of our proposed method to state-of-art partial label learning methods.

基于时序Q-learning算法的主网变电站继电保护故障快速定位方法

主网变电站继电保护故障通常是突发性的,不会持续一段时间,暂态性质不明显,快速定位效果受限,基于此,提出基于时序Q-learning算法的故障快速定位方法。在时序Q-learning中,使用不同多项式函数参数表示不同主网变电站继电保护动作,采用贪婪策略选择主网变电站继电保护动作,根据继电保护状态反馈结果更新权重,使用时序Q-learning算法进行参数训练。构建故障暂态网络的节点导纳矩阵,计算支路电压、电流,确定故障关联域。按照拓扑图论方式时序Q-learning算法搭建快速定位拓扑结构,通过分析支路电流与故障电流之间距离,计算故障相关度,完成故障快速定位。由实验结果可知,该方法故障相序与实际一致,可以分析主网变电站继电保护暂态性质,适用于复杂多变的继电保护装置。

The Correlation Study between Thinking Styles and English-Speaking Learning Strategies of Junior School Students under the Background of “Double Reduction” Policy

Based on Cognitive Style Theory,Oxford’s Speaking Learning Theory and Communicative Competence Theory,this paper adopts questionnaire method,uses Sternberg’s Thinking Style Inventory modified by Zhang Lifang,as well as Oxford’s Learning Strategies Scale,and combines with the actual situation of junior high school students’oral learning to design the questionnaire of speaking learning strategies.Eighth graders of a junior middle school in Sichuan Province are randomly selected as the research object.The statistical tool SPSS23.0 is used to conduct the descriptive analysis and Pearson correlation analysis of the questionnaire results to explore the overall situation of junior high school students’thinking styles and the use of English-speaking learning strategies as well as their correlation.According to the research,junior high school students generally tend to executive style,liberal style,conservative style,legislative style,hierarchic style.The use of speaking learning strategies is at an average level.They tend to use memory speaking strategies,social speaking strategies,affective speaking strategies,and cognitive speaking strategies.As a whole,there is a relatively significant correlation between thinking styles and speaking learning strategies.Specifically,there is a partial correlation between thinking styles and speaking learning strategies.

Correlation and Regression Analyses on the Washback effects of Achievement Oral Test on College English Learning

The purpose of this study was to investigate how washback effect of a reformed test influenced students'perspectives in their spoken English learning. This study was expected to enrich the existing literature in testing washback in English as a foreign language context.Findings indicate that the adding of oral part in achievement test does have a positive washback effect on students'learning. However, such a washback effect on students'learning is quite superficial and limited.

An Empirical Study of Depth of Vocabulary Knowledge and Vocabulary Learning Metacognitive Strategies of Non-English Majors

This paper concentrates on the depth of vocabulary knowledge and vocabulary learning metacognitive strategies used by Chinese non-English majors in an independent college.Results show the depth of vocabulary knowledge of the subjects only reaches the passing level.And metacognitive strategies are correlated significantly with the depth of vocabulary knowledge.

Feature Relationships Learning Incorporated Age Estimation Assisted by Cumulative Attribute Encoding

The research of human facial age estimation(AE)has attracted increasing attention for its wide applications.Up to date,a number of models have been constructed or employed to perform AE.Although the goal of AE can be achieved by either classification or regression,the latter based methods generally yield more promising results because the continuity and gradualness of human aging can naturally be preserved in age regression.However,the neighbor-similarity and ordinality of age labels are not taken into account yet.To overcome this issue,the cumulative attribute(CA)coding was introduced.Although such age label relationships can be parameterized via CA coding,the potential relationships behind age features are not incorporated to estimate age.To this end,in this paper we propose to perform AE by encoding the potential age feature relationships with CA coding via an implicit modeling strategy.Besides that,we further extend our model to gender-aware AE by taking into account gender variance in aging process.Finally,we experimentally validate the superiority of the proposed methodology.

Triplet Label Based Image Retrieval Using Deep Learning in Large Database

Recent days,Image retrieval has become a tedious process as the image database has grown very larger.The introduction of Machine Learning(ML)and Deep Learning(DL)made this process more comfortable.In these,the pair-wise label similarity is used tofind the matching images from the database.But this method lacks of limited propose code and weak execution of misclassified images.In order to get-rid of the above problem,a novel triplet based label that incorporates context-spatial similarity measure is proposed.A Point Attention Based Triplet Network(PABTN)is introduced to study propose code that gives maximum discriminative ability.To improve the performance of ranking,a corre-lating resolutions for the classification,triplet labels based onfindings,a spatial-attention mechanism and Region Of Interest(ROI)and small trial information loss containing a new triplet cross-entropy loss are used.From the experimental results,it is shown that the proposed technique exhibits better results in terms of mean Reciprocal Rank(mRR)and mean Average Precision(mAP)in the CIFAR-10 and NUS-WIPE datasets.

Communication Skills Training for Residents: Which Variables Predict Learning of Skills?

Objective: This study aimed to determine variables associated (predictors and correlates) with the learning of assessment and supportive skills in the context of a communication skills training for medical residents. Methods: Learning was measured by comparing residents’ communication skills in a simulated consultation before and after a communication skills training. Communication skills were transcribed and tagged with a computer-assisted program. Potential variables associated with learning (residents’ characteristics, contextual characteristics and pre-training communication skills) were measured before the training and entered in regression analysis. Results: Fifty-six residents followed the training between 2002 and 2006. Poor pre-training assessment and supportive skills predicted the respective learning of these skills. Better assessment skills’ learning was predicted by copings (i.e. lower level of emotional coping), lower levels of self-efficacy and depersonalization. Better supportive skills’ learning was predicted by a lower work experience and associated with a higher training attendance rate. Conclusions: Predictors and correlates of assessment and supportive skills learning were different. Trainers needed to detect certain residents’ characteristics (i.e. depersonalization) in order to optimize assessment skills learning. Trainers needed to be aware that supportive skills are difficult to learn and to teach and may need more training hours.

An Enhanced Automatic Arabic Essay Scoring System Based on Machine Learning Algorithms

Despite the extensive effort to improve intelligent educational tools for smart learning environments,automatic Arabic essay scoring remains a big research challenge.The nature of the writing style of the Arabic language makes the problem even more complicated.This study designs,implements,and evaluates an automatic Arabic essay scoring system.The proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing tasks.Then,it comprises two main components:the grading engine and the adaptive fusion engine.The grading engine employs string-based and corpus-based similarity algorithms separately.After that,the adaptive fusion engine aims to prepare students’scores to be delivered to different feature selection algorithms,such as Recursive Feature Elimination and Boruta.Then,some machine learning algorithms such as Decision Tree,Random Forest,Adaboost,Lasso,Bagging,and K-Nearest Neighbor are employed to improve the suggested system’s efficiency.The experimental results in the grading engine showed that Extracting DIStributionally similar words using the CO-occurrences similarity measure achieved the best correlation values.Furthermore,in the adaptive fusion engine,the Random Forest algorithm outperforms all other machine learning algorithms using the(80%–20%)splitting method on the original dataset.It achieves 91.30%,94.20%,0.023,0.106,and 0.153 in terms of Pearson’s Correlation Coefficient,Willmot’s Index of Agreement,Mean Square Error,Mean Absolute Error,and Root Mean Square Error metrics,respectively.

Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data set.the same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall prediction.With this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)Technique.The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes.Primarily,data pre-processing and correlation matrix(CM)based feature selection processes are carried out.In addition,deep belief network(DBN)model is applied for the effective prediction of rainfall data.Moreover,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO algorithm.Finally,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN model.For demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct measures.The simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.

Misfire identification of automobile engines based on wavelet packet and extreme learning machine

Due to non-stationary characteristics of the vibration signal acquired from cylinder head,a misfire fault diagnosis system of automobile engines based on correlation coefficient gained by wavelet packet and extreme learning machine(ELM)is proposed.Firstly,the original signal is decomposed by wavelet packet,and correlation coefficients between the reconstructed signal of each sub-band and the original signal as well as the energy entropy of each sample are obtained.Then,the eigenvectors established by the correlation coefficients method and the energy entropy method fused with kurtosis are inputted to the four kinds of classifiers including BP neural network,KNN classifier,support vector machine and ELM respectively for training and testing.Experimental results show that the method proposed in this paper can effectively reflect the differences that the fault produces and identify the single-cylinder misfire accurately,which has the advantages of higher accuracy and shorter training time.

MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

Mobile clouds are the most common medium for aggregating,storing,and analyzing data from the medical Internet of Things(MIoT).It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and prediction.Among the various disease,skin cancer was the wide variety of cancer,as well as enhances the endurance rate.In recent years,many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors,including malignant melanoma(MM)and other skin cancers.However,accurate cancer detection was not performed with minimum time consumption.In order to address these existing problems,a novel Multidimensional Bregman Divergencive Feature Scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification(MBDFS-CPRRDLC)technique is introduced for detecting cancer at an earlier stage.The MBDFS-CPRRDLC performs skin cancer detection using different layers such as input,hidden,and output for feature selection and classification.The patient information is composed of IoT.The patient information was stored in mobile clouds server for performing predictive analytics.The collected data are sent to the recurrent deep learning classifier.In the first hidden layer,the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature Scaling technique to find the significant features for disease identification resulting in decreases time consumption.Followed by,the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training data.This process is repeatedly performed until the error gets minimized.In this way,disease classification is accurately performed with higher accuracy.Experimental evaluation is carried out for factors namely Accuracy,precision,recall,F-measure,as well as cancer detection time,by the amount of patient data.The observed result confirms that the proposed MBDFS-CPRRDLC technique increases accuracy as well as lesser cancer detection time compared to the conventional approaches.