A Study of Multimodal Intelligent Adaptive Learning System and Its Pattern of Promoting Learners’Online Learning Engagement

As the field of artificial intelligence continues to evolve,so too does the application of multimodal learning analysis and intelligent adaptive learning systems.This trend has the potential to promote the equalization of educational resources,the intellectualization of educational methods,and the modernization of educational reform,among other benefits.This study proposes a construction framework for an intelligent adaptive learning system that is supported by multimodal data.It provides a detailed explanation of the system’s working principles and patterns,which aim to enhance learners’online engagement in behavior,emotion,and cognition.The study seeks to address the issue of intelligent adaptive learning systems diagnosing learners’learning behavior based solely on learning achievement,to improve learners’online engagement,enable them to master more required knowledge,and ultimately achieve better learning outcomes.

Noise-tolerate and adaptive coefficient zeroing neural network for solving dynamic matrix square root

The solving of dynamic matrix square root(DMSR)problems is frequently encountered in many scientific and engineering fields.Although the original zeroing neural network is powerful for solving the DMSR,it cannot vanish the influence of the noise perturbations,and its constant-coefficient design scheme cannot accelerate the convergence speed.Therefore,a noise-tolerate and adaptive coefficient zeroing neural network(NTACZNN)is raised to enhance the robust noise immunity performance and accelerate the conver-gence speed simultaneously.Then,the global convergence and robustness of the pro-posed NTACZNN are theoretically analysed under an ideal environment and noise-perturbed circumstances.Furthermore,some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem solution.Compared with some existing ZNNs,the proposed NTACZNN possesses advanced performance in terms of noise tolerance,solution accuracy,and convergence rate.

I-Quiz:An Intelligent Assessment Tool for Non-Verbal Behaviour Detection

Electronic learning(e-learning)has become one of the widely used modes of pedagogy in higher education today due to the convenience and flexibility offered in comparison to traditional learning activities.Advancements in Information and Communication Technology have eased learner connectivity online and enabled access to an extensive range of learning materials on the World Wide Web.Post covid-19 pandemic,online learning has become the most essential and inevitable medium of learning in primary,secondary and higher education.In recent times,Massive Open Online Courses(MOOCs)have transformed the current education strategy by offering a technology-rich and flexible form of online learning.A key component to assess the learner’s progress and effectiveness of online teaching is the Multiple Choice Question(MCQ)assessment in most of the MOOC courses.Uncertainty exists on the reliability and validity of the assessment component as it raises a qualm whether the real knowledge acquisition level reflects upon the assessment score.This is due to the possibility of random and smart guesses,learners can attempt,as MCQ assessments are more vulnerable than essay type assessments.This paper presents the architecture,development,evaluation of the I-Quiz system,an intelligent assessment tool,which captures and analyses both the implicit and explicit non-verbal behaviour of learner and provides insights about the learner’s real knowledge acquisition level.The I-Quiz system uses an innovative way to analyse the learner non-verbal behaviour and trains the agent using machine learning techniques.The intelligent agent in the system evaluates and predicts the real knowledge acquisition level of learners.A total of 500 undergraduate engineering students were asked to attend an on-Screen MCQ assessment test using the I-Quiz system comprising 20 multiple choice questions related to advanced C programming.The non-verbal behaviour of the learner is recorded using a front-facing camera during the entire assessment period.The resultant dataset of non-verbal behaviour and question-answer scores is used to train the random forest classifier model to predict the real knowledge acquisition level of the learner.The trained model after hyperparameter tuning and cross validation achieved a normalized prediction accuracy of 85.68%.

Optimal fusion‐based localization method for tracking of smartphone user in tall complex buildings

In the event of a fire breaking out or in other complicated situations,a mobile computing solution combining the Internet of Things and wearable devices can actually assist tracking solutions for rescuing and evacuating people in multistory structures.Thus,it is crucial to increase the positioning technology's accuracy.The sequential Monte Carlo(SMC)approach is used in various applications such as target tracking and intelligent surveillance,which rely on smartphone‐based inertial data sequences.However,the SMC method has intrinsic flaws,such as sample impoverishment and particle degeneracy.A novel SMC approach is presented,which is built on the weighted differential evolution(WDE)algorithm.Sequential Monte Carlo approaches start with random particle placements and arrives at the desired distribution with a slower variance reduction,like in a high‐dimensional space,such as a multistory structure.Weighted differential evolution is included before the resampling procedure to guarantee the appropriate variety of the particle set,prevent the usage of an inadequate number of valid samples,and preserve smartphone user position accuracy.The values of the smartphone‐based sensors and BLE‐beacons are set as input to the SMC,which aids in fast approximating the posterior distributions,to speed up the particle congregation process in the proposed SMC‐based WDE approach.Lastly,the robustness and efficacy of the suggested technique more accurately reflect the actual situation of smartphone users.According to simulation findings,the suggested approach provides improved location estimation with reduced localization error and quick convergence.The results confirm that the proposed optimal fusion‐based SMC‐WDE scheme performs 9.92%better in terms of MAPE,15.24%for the case of MAE,and 0.031%when evaluating based on the R2 Score.

Intelligent speed adaptation for visibility technology affects drivers’speed selection along curves with sight limitations

Sight obstructions along road curves can lead to a crash if the driver is not able to stop the vehicle in time.This is a particular issue along curves with limited available sight,where speed management is necessary to avoid unsafe situations(e.g.,driving off the road or invading the other traffic lane).To solve this issue,we proposed a novel intelligent speed adaptation(ISA)system for visibility,called V-ISA(intelligent speed adaptation for visibility).It estimates the real-time safe speed limits based on the prevailing sight conditions.V-ISA comes with three variants with specific feedback modalities(1)visual and(2)auditory information,and(3)direct intervention to assume control over the vehicle speed.Here,we investigated the efficiency of each of the three V-ISA variants on driving speed choice and lateral behavioural response along road curves with limited and unsafe available sight distances,using a driving simulator.We also considered curve road geometry(curve direction:rightward vs.leftward).Sixty active drivers were recruited for the study.While half of them(experimental group)tested the three V-ISA variants(and a V-ISA off condition),the other half always drove with the V-ISA off(validation group).We used a linear mixed-effect model to evaluate the influence of V-ISA on driver behaviour.All V-ISA variants were efficient at reducing speeds at entrance points,with no discernible negative impact on driver lateral behaviour.On rightward curves,the V-ISA intervening variant appeared to be the most effective at adapting to sight limitations.Results of the current study implies that V-ISA might assist drivers to adjust their operating speed as per prevailing sight conditions and,consequently,establishes safer driving conditions.

An analysis on possibility of intelligent speed adaptation in terms of drivers' consciousness

Intelligent speed adaptation （ISA） is considered as an effective measure to reduce number of traffic accidents in the field of intelligent transportation systems （ITS）. On the other hand, its effects for traffic safety are still doubted by many people. To make the possibility analysis, an experiment is conducted by using driving simulator. Regarding ISA ap- proaches, there are three modes： mandatory, voluntary and advisory. Among them, the advisory type seems to be the easiest one to introduce. Therefore, we focus on the advisory mode in this study by considering ISA just at the beginning stage in Japan. The experiment consists of four steps： without ISA, ISA using pictures, ISA using voices and again without ISA. The outputs obtained from the driving simulator are analyzed combined with the consciousness of the participants. The experiment shows that the ISA can improve recognition of speed limitation especially for people who have random rambling or looking aside tendency. Furthermore, the ISA especially when using voices can contribute in changing the consciousness of people who are aggressive in driving. Their driving speeds can reduce so that positive effects on traffic safety can be concluded.

Revisiting the ODE Method for Recursive Algorithms:Fast Convergence Using Quasi Stochastic Approximation

Several decades ago,Profs.Sean Meyn and Lei Guo were postdoctoral fellows at ANU,where they shared interest in recursive algorithms.It seems fitting to celebrate Lei Guo’s 60 th birthday with a review of the ODE Method and its recent evolution,with focus on the following themes:The method has been regarded as a technique for algorithm analysis.It is argued that this viewpoint is backwards:The original stochastic approximation method was surely motivated by an ODE,and tools for analysis came much later(based on establishing robustness of Euler approximations).The paper presents a brief survey of recent research in machine learning that shows the power of algorithm design in continuous time,following by careful approximation to obtain a practical recursive algorithm.While these methods are usually presented in a stochastic setting,this is not a prerequisite.In fact,recent theory shows that rates of convergence can be dramatically accelerated by applying techniques inspired by quasi Monte-Carlo.Subject to conditions,the optimal rate of convergence can be obtained by applying the averaging technique of Polyak and Ruppert.The conditions are not universal,but theory suggests alternatives to achieve acceleration.The theory is illustrated with applications to gradient-free optimization,and policy gradient algorithms for reinforcement learning.