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adaptive learning path recommendation model for examination-oriented education

The Journal of China Universities of Posts and Telecommunications 2022年第4期29卷 77-88页

作者： Wang Jian Qiao Kuoyuan Yuan Yanlei Liu Xiaole Yang Jian School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China Research and Development Department Tongfang Knowledge Network Technology Company LimitedBeijing 100192China The Teaching Affaires Office Zhengzhou Hi-Tech Zone Langyuehui Foreign Language Middle SchoolZhengzhou 450001China Yunnan Key Laboratory of Smart City in Cyberspace Security Yuxi Normal UniversityYuxi 653100China

adaptive learning paths provide individual learning objectives that best match a learner’s characteristics.This is especially helpful when learners need to balance limited available learning time and multiple learning objectives.The automatic generation of personalized learning paths to improve learning efficiency has therefore attracted significant interest.However,most current research only focuses on providing learners with adaptive objects and sequences according to their own interests or learning goals given a normal amount of time or ordinary conditions.There is little research that can help learners to obtain the most important knowledge for a test in the shortest time possible,which is a typical scenario in exanimation-oriented education systems.This study aims to solve this problem by introducing a new approach that builds on existing methods.First,the eight properties in Gardner’s multiple intelligence theory are introduced into the present knowledge and learner models to define the relationship between learning objects(LOs)and learners,thereby improving recommendation accuracy rates.Then,a novel adaptive learning path recommendation model is presented where viable knowledge topologies,knowledge bases and the previously-established properties relating to a learner’s ability are combined by Dempster-Shafer(D-S)evidence theory.A series of practical experiments were performed to assess the approach’s adaptability,the appropriateness of the selected evidence and the effectiveness of the recommendations.In the results,it was found that the proposed learning path recommendation model helped learners learn the most important elements and obtain superior test grades when confronted with limited time for learning.

关键词： adaptive learning learning path recommendation Dempster-Shaferevidence theory knowledge model learner model

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Frequency Domain adaptive learning Algorithm for Thoracic Electrical Bioimpedance Enhancement

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Computers, Materials & Continua 2022年第9期72卷 5713-5726页

作者： Md Zia Ur Rahman S.Rooban P.Rohini M.V.S.Ramprasad Pradeep Vinaik Kodavanti Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationK L UniversityVaddeswaramGuntur522502Andhra PradeshIndia Department of Computer Science and Engineering Faculty of Science and TechnologyICFAI Foundation for Higher EducationHyderabad500082TelanganaIndia Department of Electrical Electronics and Communication EngineeringGITAM Institute of TechnologyGITAM Deemed to be UniversityVisakhapatnam530045Andhra PradeshIndia

The Thoracic Electrical Bioimpedance(TEB)helps to determine the stroke volume during cardiac arrest.While measuring cardiac signal it is contaminated with artifacts.The commonly encountered artifacts are Baseline wander(BW)and Muscle artifact(MA),these are physiological and nonstationary.As the nature of these artifacts is random,adaptive filtering is needed than conventional fixed coefficient filtering techniques.To address this,a new block based adaptive learning scheme is proposed to remove artifacts from TEB signals in clinical scenario.The proposed block least mean square(BLMS)algorithm is mathematically normalized with reference to data and error.This normalization leads,block normalized LMS(BNLMS)and block error normalized LMS(BENLMS)algorithms.Various adaptive artifact cancellers are developed in both time and frequency domains and applied on real TEB quantities contaminated with physiological signals.The ability of these techniques is measured by calculating signal to noise ratio improvement(SNRI),Excess Mean Square Error(EMSE),and Misadjustment(Mad).Among the considered algorithms,the frequency domain version of BENLMS algorithm removes the physiological artifacts effectively then the other counter parts.Hence,this adaptive artifact canceller is suitable for real time applications like wearable,remove health care monitoring units.

关键词： adaptive learning artifact canceller block processing frequency domain thoracic electrical bioimpedance

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XA-GANomaly: An Explainable adaptive Semi-Supervised learning Method for Intrusion Detection Using GANomaly

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Computers, Materials & Continua 2023年第7期76卷 221-237页

作者： Yuna Han Hangbae Chang Department of Security Convergence Graduate SchoolChung-Ang UniversitySeoul06974Korea Department of Industrial Security Chung-Ang UniversitySeoul06974Korea

Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission.Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry.However,real-time training and classifying network traffic pose challenges,as they can lead to the degradation of the overall dataset and difficulties preventing attacks.Additionally,existing semi-supervised learning research might need to analyze the experimental results comprehensively.This paper proposes XA-GANomaly,a novel technique for explainable adaptive semi-supervised learning using GANomaly,an image anomalous detection model that dynamically trains small subsets to these issues.First,this research introduces a deep neural network(DNN)-based GANomaly for semi-supervised learning.Second,this paper presents the proposed adaptive algorithm for the DNN-based GANomaly,which is validated with four subsets of the adaptive dataset.Finally,this study demonstrates a monitoring system that incorporates three explainable techniques—Shapley additive explanations,reconstruction error visualization,and t-distributed stochastic neighbor embedding—to respond effectively to attacks on traffic data at each feature engineering stage,semi-supervised learning,and adaptive learning.Compared to other single-class classification techniques,the proposed DNN-based GANomaly achieves higher scores for Network Security Laboratory-Knowledge Discovery in Databases and UNSW-NB15 datasets at 13%and 8%of F1 scores and 4.17%and 11.51%for accuracy,respectively.Furthermore,experiments of the proposed adaptive learning reveal mostly improved results over the initial values.An analysis and monitoring system based on the combination of the three explainable methodologies is also described.Thus,the proposed method has the potential advantages to be applied in practical industry,and future research will explore handling unbalanced real-ti

关键词： Intrusion detection system(IDS) adaptive learning semi-supervised learning explainable artificial intelligence(XAI) monitoring system

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adaptive learning with Large Variability of Teaching Signals for Neural Networks and Its Application to Motion Control of an Industrial Robot

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International Journal of Automation and computing 2011年第1期8卷 54-61页

作者： Fusaomi Nagata Keigo Watanabe Department of Mechanical Engineering Faculty of Engineering Tokyo University of Science Department of Intelligent Mechanical Systems Graduate School of Natural Science and Technology Okayama University

Recently, various control methods represented by proportional-integral-derivative （PID） control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward controllers such as gravity compensator, Coriolis/centrifugal force compensator and friction compensators have been built in the controller. Generally, it causes heavy computational load when calculating the compensating value within a short sampling period. In this paper, integrated recurrent neural networks are applied as a feedforward controller for PUMA560 manipulator. The feedforward controller works instead of gravity and Coriolis/centrifugal force compensators. In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals. The tuning is complicated because it is being conducted by trial and error. Especially, when the scale of teaching signal is large, the problem becomes crucial. To cope with the problem which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed. The learning techniques and control effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.

关键词： Neural networks large-scale teaching signal sigmoid function adaptive learning servo system PUMA560 manipulator trajectory following control nonlinear control.

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adaptive deep learning for head and neck cancer detection using hyperspectral imaging

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Visual Computing for Industry,Biomedicine,and Art 2019年第1期2卷 164-175页

作者： Ling Ma Guolan Lu Dongsheng Wang Xulei Qin Zhuo Georgia Chen Baowei Fei Department of Radiology and Imaging Sciences Emory UniversityAtlantaGA 30322USA College of Software Nankai UniversityTianjin 300350People’s Republic of China Department of Hematology and Medical Oncology Emory UniversityAtlantaGA 30322USA Department of Bioengineering The University of Texas at DallasRichardsonTX 75080USA Department of Radiology The University of Texas Southwestern Medical CenterDallasTX 75390USA

It can be challenging to detect tumor margins during surgery for complete resection.The purpose of this work is to develop a novel learning method that learns the difference between the tumor and benign tissue adaptively for cancer detection on hyperspectral images in an animal model.Specifically,an auto-encoder network is trained based on the wavelength bands on hyperspectral images to extract the deep information to create a pixel-wise prediction of cancerous and benign pixel.According to the output hypothesis of each pixel,the misclassified pixels would be reclassified in the right prediction direction based on their adaptive weights.The auto-encoder network is again trained based on these updated pixels.The learner can adaptively improve the ability to identify the cancer and benign tissue by focusing on the misclassified pixels,and thus can improve the detection performance.The adaptive deep learning method highlighting the tumor region proved to be accurate in detecting the tumor boundary on hyperspectral images and achieved a sensitivity of 92.32%and a specificity of 91.31%in our animal experiments.This adaptive learning method on hyperspectral imaging has the potential to provide a noninvasive tool for tumor detection,especially,for the tumor whose margin is indistinct and irregular.

关键词： Hyperspectral imaging Deep learning adaptive learning Noninvasive cancer detection

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Designing and evaluating assessment and learning in adaptive learning systems

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语言测试与评价 2022年第1期 94-115,119,120页

作者： XI XIAOMING Hong Kong Examinations and Assessment Authority

The recent emergence of adaptive language learning systems calls for conceptual work to guide the design of assessment and learning in an adaptive environment.Although adaptive learning might have been touted as a universal cure for learning problems,many adaptive language learning systems fall short of educators’expectations,partly due to a lack of standards and best practices in this area.To fill this gap,this paper proposes some major considerations in designing a high-quality assessment and learning experience in adaptive learning and ways to evaluate an adaptive learning system.The architecture of adaptive learning is decomposed,with a chain of inferences supporting the overall efficacy of an adaptive learning system presented,including user property representation,user property estimation,content representation,user interaction representation,and user interaction impact.A detailed analysis of key validity issues is provided for each inference,which motivates the major considerations in designing and evaluating assessment and learning.The paper first provides an overview of different types of assessment used in adaptive learning and an analysis of the assessment approach,priorities,and design considerations of each to optimize its use in adaptive learning.Then it proposes a framework for evaluating different aspects of an adaptive learning system.Some special connections are made to models,techniques,designs,and technologies specific to language learning and assessment,bringing more relevance to adaptive language learning solutions.Through establishing some guidelines on key aspects to evaluate and how to evaluate them,the work intends to bring more rigor to the field of adaptive language learning systems.

关键词： assessment adaptive learning evaluation framework efficacy validity

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adaptive Metric learning for Dimensionality Reduction

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Journal of Computer and Communications 2022年第12期10卷 95-112页

作者： Lihua Chen Peiwen Wei Zhongzhen Long Yufeng Yu Department of Statistics Guangzhou University Guangzhou China Shenzhen Securities Communication Co. Ltd. Shenzhen China

Finding a suitable space is one of the most critical problems for dimensionality reduction. Each space corresponds to a distance metric defined on the sample attributes, and thus finding a suitable space can be converted to develop an effective distance metric. Most existing dimensionality reduction methods use a fixed pre-specified distance metric. However, this easy treatment has some limitations in practice due to the fact the pre-specified metric is not going to warranty that the closest samples are the truly similar ones. In this paper, we present an adaptive metric learning method for dimensionality reduction, called AML. The adaptive metric learning model is developed by maximizing the difference of the distances between the data pairs in cannot-links and those in must-links. Different from many existing papers that use the traditional Euclidean distance, we use the more generalized l2,p-norm distance to reduce sensitivity to noise and outliers, which incorporates additional flexibility and adaptability due to the selection of appropriate p-values for different data sets. Moreover, considering traditional metric learning methods usually project samples into a linear subspace, which is overstrict. We extend the basic linear method to a more powerful nonlinear kernel case so that well capturing complex nonlinear relationship between data. To solve our objective, we have derived an efficient iterative algorithm. Extensive experiments for dimensionality reduction are provided to demonstrate the superiority of our method over state-of-the-art approaches.

关键词： adaptive learning Kernel learning Dimension Reduction Pairwise Constraints

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Enhancing Next-Item Recommendation Through adaptive User Group Modeling

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Journal of Social Computing 2023年第2期4卷 112-124页

作者： Nengjun Zhu Lingdan Sun Jian Cao Xinjiang Lu Runtong Li the School of Computer Engineering and Science Shanghai UniversityShanghai 200444China the Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China the Business Intelligence Lab Baidu ResearchBeijing 100085China the Department of Electronic Engineering Tsinghua UniversityBeijing 100084China

Session-based recommender systems are increasingly applied to next-item recommendations.However,existing approaches encode the session information of each user independently and do not consider the interrelationship between users.This work is based on the intuition that dynamic groups of like-minded users exist over time.By considering the impact of latent user groups,we can learn a user’s preference in a better way.To this end,we propose a recommendation model based on learning user embeddings by modeling long and short-term dynamic latent user groups.Specifically,we utilize two network units to learn users’long and short-term sessions,respectively.Meanwhile,we employ two additional units to determine the affiliation of users with specific latent groups,followed by an aggregation of these latent group representations.Finally,user preference representations are shaped comprehensively by considering all these four aspects,based on an attention mechanism.Moreover,to avoid setting the number of groups manually,we further incorporate an adaptive learning unit to assess the necessity for creating a new group and learn the representation of emerging groups automatically.Extensive experiments prove our model outperforms multiple state-of-the-art methods in terms of Recall,mean average precision(mAP),and area under curve(AUC)metrics.

关键词： session-based recommender user group modeling attention mechanism adaptive learning

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An adaptive system for detecting malicious queries in web attacks

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Science China(Information Sciences) 2018年第3期61卷 175-190页

作者： Ying DONG Yuqing ZHANG Hua MA Qianru WU Qixu LIU Kai WANG Wenjie WANG National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences School of Mathematics and Statistics Xidian University Security Department Alibaba Group Zhanlu Laboratory Tencent Incorporation

Web request query strings(queries), which pass parameters to a referenced resource, are always manipulated by attackers to retrieve sensitive data and even take full control of victim web servers and web applications. However, existing malicious query detection approaches in the literature cannot cope with changing web attacks. In this paper, we introduce a novel adaptive system(AMOD) that can adaptively detect web-based code injection attacks, which are the majority of web attacks, by analyzing queries. We also present a new adaptive learning strategy, called SVM HYBRID, leveraged by our system to minimize manual work. In the evaluation, an up-to-date detection model is trained on a ten-day query dataset collected from an academic institute’s web server logs. The evaluation shows our approach overwhelms existing approaches in two respects. Firstly, AMOD outperforms existing web attack detection methods with an F-value of 99.50%and FP rate of 0.001%. Secondly, the total number of malicious queries obtained by SVM HYBRID is3.07 times that by the popular support vector machine adaptive learning(SVM AL) method. The malicious queries obtained can be used to update the web application firewall(WAF) signature library.

关键词： web attacks adaptive learning intrusion detection anomaly detection SVM

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Optimized adaptive Fuzzy Forecasting System with Application

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International Journal of Plant Engineering and Management 1998年第1期3卷 39-49页

作者： Yu Bin, Zhong Muliang, Zhang Hao, Mao Zongyuan, Zhou QijieDepartment of Automatic Control Engineering, South China University of TechnologyGuangzhou, 510641, P.R. China

In this paper, a fuzzy forecasting system is designed and implemented by which an original forecasting model can be obtained by data learning. The model parameters can then be adaptively optimized through gradient information of real-time data. Thus, the system is of extinguished adaptive feature and self-learning capability. Afterwards, experimental research efforts are put forward to carry out electric power load forecasting. Experimental results demonstrate the satisfactory performances of the intelligent forecasting system.

关键词： fuzzy system adaptive learning short-term load forecasting economic forecasting

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