Architecture Of Learning System Research Articles

Analyzing the Software Architecture of ML-based Covid-19 Detection System: Future Challenges and Opportunities

Two major study topics have emerged because of the challenges in software architecture and ML working together, as modern software systems produce a vast amount of data that is supported particularly by machine learning (ML), and artificial intelligence (AI) to produce useful insights. Software architecture for machine learning systems that primarily concerned with creating architectural methods for creating ML systems more effectively; ii) ML for Software architectures is concerned with creating ML methods for better-developing software systems. This study focuses on the ML-based software systems' architecture to highlight the many architectural methods currently in use. To more clearly identify a set of acceptable standards for designing ML-based software systems, we explore four crucial components of software architecture in this work that demand the focus of ML and software developers. These areas are based on an ML-based software system for addressing challenges in the COVID-19 detecting system.

Fully automated learning and predict price of aquatic products in Taiwan wholesale markets using multiple machine learning and deep learning methods

The aquatic market price is an essential indicator for fishermen in making decisions regarding conveyance, fish-catching, and wholesale strategies. As a guide for the aquaculture industry, understanding market prices, the geographical distribution of fish prices, and the prediction of fish prices are vital. In Taiwanese fisheries, however, predicting aquatic prices is challenging due to their drastic fluctuations caused by tropical and subtropical climate variations, export/import fish quantity changes, and political and economic situation uncertainty. Therefore, instead of using a single machine learning model to address the price fluctuations, we propose a hybrid fish price prediction model integrating multiple machine learning and deep learning models to identify and predict fish price dynamics in various wholesale markets. The extensive experimental results on actual market data from the Taiwan Council of Agriculture (COA) show that the proposed models can achieve >90% accuracy in predicting future aquatic prices. Additionally, we developed a fully automated learning and prediction system architecture on the cloud, allowing us to acquire data automatically and fine-tune the AI models continuously to achieve better performance with long-term operability.

Targeted Display Advertising using Machine Learning

This paper delves into the intricate challenges of problem formulation and data representation in the context of a large-scale machine learning system for targeted display advertising. Unlike traditional models, this system is not just conceptual but has been operational for years across thousands of advertising campaigns. Since obtaining ideal training data is cost-prohibitive, data is sourced from related domains and tasks and then adapted for the target task. The paper outlines the architecture of this multi-stage transfer learning system, emphasizing the problem formulation aspects. Extensive experiments demonstrate the value of each transfer stage. Real-world results with diverse advertising clients from various industries showcase the system's performance. The paper concludes with valuable insights gained from over half a decade of work on this complex, widely deployed machine learning system.

Automated deep learning system for power line inspection image analysis and processing: Architecture and design issues

The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible

The design method of primary school English learning system based on intelligent push

In the reform and development of modern education, electronic learning system with computer technology as the core has been popularized and applied, which not only reflects the unique advantages of modern information technology, but also fully mobilizes students' interest in learning, improves the traditional education guidance model, and enhances the effect of primary school English learning. Especially in the context of the new curriculum reform, the traditional monotonous primary school English learning mode has been unable to meet the needs of students, and it is difficult to cultivate professional English talents needed in the new era, so it is necessary to combine artificial intelligence to build a primary school English learning system. On the basis of understanding the research status of primary school English learning system, this paper mainly explores the architecture of primary school English learning system with intelligent push as the core, and designs and implements learning resource push application by taking Android mobile phone as an example. The final result proves that the primary school English learning system based on intelligent push is more in line with the needs of education reform.

Use of cognitive load measurements to design a new architecture of intelligent learning systems

In the context of learning environments, the learner’s attention and mental effort are of primary interest in the process of acquiring knowledge. Due to the skills and abilities of each learner, there is a growing need for generic and adaptive environments. In this work, we introduce a new architecture for such environments to assist the learner with a multi-agent-based approach. Using Electroencephalogram, this architecture generates learning content and interactions adapted for each learner. Moreover, this work presents a new unsupervised approach to estimate and recognize the learner’s cognitive load based on the Standardized Euclidean Distance (SED) and the Power Spectral Density (PSD) of brain rhythms within low frequencies, namely Theta[4–7 Hz] (θ) and Alpha [8–11 Hz] (α). The learner’s outcomes and estimated mental efforts are combined in the evaluation process using a fuzzy logic-based approach. Three experimental protocols are adopted in order to validate our study. These protocols are based on cognitive tasks with different difficulty levels. Experimental results show that PSD in θ and α bands in the occipital lobe accurately describe changes in the learner’s mental efforts and cognitive load according to the cognitive task difficulty level. Based on the Cohen Kappa coefficient, our cognitive load estimation approach, using α, is compared to an existing cognitive load index from the literature. This performance assessment process revealed large values (k≥0.48) in the occipital lobe, which reflects the efficiency of the proposed approach. Results from this study are mainly used in educational engineering and reeducation in order to subjectively assess the approaches and treatments offered in these contexts.

SUMO Simulations for Federated Learning in Communicating Autonomous Vehicles

In transportation, a vehicle's route is one of the most private information. However, to mutually learn some phenomena in a city, for example, parking lot occupancies, we might have to reveal information about it. In this paper, we focus on assessing the privacy loss in a vehicular federated machine learning system. For the analysis, we used the Monaco SUMO Traffic Scenario (MoST). We also used the simulation inputs as statistical data to calculate privacy loss metrics. Results show that a vehicular federated machine learning system may pose a smaller privacy threat than individual learning, but its performance is lower compared to a centralized learning approach.
 Due to the vast amount of data and processing time, we also describe a method to build a Docker image of SUMO together with a software client-server architecture for SUMO-based learning systems on multiple computers.

Архитектура учебной системы с индивидуализацией обучения на основе накопленных данных результатов автоматизированной проверки заданий

в условиях первоочередной потребности в технологической независимости Российской Федерации от системы образования ждут гибкого подхода к обучению, при котором каждый студент может полностью раскрыть свой потенциал и получить максимальную пользу от обучения. Для внедрения индивидуализации образования применяются технологии адаптивного обучения, которые построены на регулярной проверке знаний и автоматическом выборе темы и уровня сложности материала, который должен изучать студент для достижения наилучших доступных ему результатов по итогам обучения. Современные адаптивные технологии разрабатываются с использованием больших объемов накопленных данных, которые формируют цифровой след обучения. Авторы применили для исследования процесса внедрения адаптивных технологий имитационное моделирование методом системной динамики. Разработанная авторами модель процесса внедрения описывает систему взаимосвязанных процессов обучения студентов, накопления данных для разработки, реализации адаптивной технологии, апробации адаптивной технологии и ее распространения. По результатам анализа для каждого этапа внедрения предложены архитектурные решения, нацеленные на ускорение процесса внедрения технологий индивидуализированного обучения за счет построения учебной среды, состоящей из локальных систем учебных заведений, единых внешних образовательных платформ и авторских учебных платформ. as the Russian Federation strives for technological independence, the educational system is expected to present a new, agile approach to learning to help each student unleash their potential and get the top value from learning. Adaptive learning is a way of customizing the learning path based on frequent assessment and automatic selection of the content and its level of complexity that the student should study to achieve the best academic performance available. Modern adaptive learning uses large amounts of student digital footprints. The authors applied simulation and system dynamics to analyze the adaptive learning implementation. The model defines a system of interrelated learning processes, data accumulation for adaptive learning support, adaptive technology validation, and dissemination. We also proposed architectural solutions for each stage of the system implementation. The solutions would accelerate the adoption of adaptive learning by building an educational environment consisting of local educational systems, unified external educational platforms, and instructors’ educational platforms.

Fault detection and classification of an HVDC transmission line using a heterogenous multi‐machine learning algorithm

This paper presents a novel integrated multi-Machine Learning (ML) system architecture for the protection of bipolar HVDC transmission line in which different ML models of Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) are used for fault detection and classification. The KNN fault type classifier is designed as a dual-purpose module, which not only detects the fault type but also acts as a redundant module for unsure fault declaration from the startup unit. Gradients and standard deviations of DC current, voltage, harmonic current, and a correlation coefficient between the aerial and zero modes of DC current are appropriate feature vector extracted from single-end signal measurement. Overall, 154 training cases and 53 main test cases are obtained by simulating various fault and non-fault states on a ±650 kV-1000 km Current Source Converter (CSC)–HVDC using an EMTDC/PSCAD platform. The ML modules are trained in MATLAB and tested under different severe conditions with a total of 2220 test cases. Thanks to the appropriate feature vector and the proposed system architecture, the obtained results show that the proposed algorithm is effective enough to detect and distinguish a variety of internal faults and pseudo-faults/external faults. Also, it needs low training data requirements.

New Opportunities: Teacher Librarians Managing Learning Objects

Digital learning objects are new kinds of resources which teacher librarians will be required to manage and make accessible to teachers and students. In Australia there are currently large-scale national and state initiatives underway to develop a critical mass of learning objects. The development of a Learning System Architecture has also become a vital step to make it possible to manage these learning objects. Packages that will enable students and teachers to communicate, collaborate, locate and access resources within intellectual property arrangements, assemble digital resources into learning sequences, assess and report are all necessary requirements. The Learning System Architecture emerging in Australia enables these disparate systems to function together as seamless and interoperable packages. A new profile of teacher librarian competency is being developed in Tasmania to assist with planning the professional learning needs of this group. The new profile includes understandings and experience of information communication technologies and online learning. Managing these new digital resources to support the teaching and learning is a key new professional role for teacher librarians.

A global framework for a systemic view of brain modeling

The brain is a complex system, due to the heterogeneity of its structure, the diversity of the functions in which it participates and to its reciprocal relationships with the body and the environment. A systemic description of the brain is presented here, as a contribution to developing a brain theory and as a general framework where specific models in computational neuroscience can be integrated and associated with global information flows and cognitive functions. In an enactive view, this framework integrates the fundamental organization of the brain in sensorimotor loops with the internal and the external worlds, answering four fundamental questions (what, why, where and how). Our survival-oriented definition of behavior gives a prominent role to pavlovian and instrumental conditioning, augmented during phylogeny by the specific contribution of other kinds of learning, related to semantic memory in the posterior cortex, episodic memory in the hippocampus and working memory in the frontal cortex. This framework highlights that responses can be prepared in different ways, from pavlovian reflexes and habitual behavior to deliberations for goal-directed planning and reasoning, and explains that these different kinds of responses coexist, collaborate and compete for the control of behavior. It also lays emphasis on the fact that cognition can be described as a dynamical system of interacting memories, some acting to provide information to others, to replace them when they are not efficient enough, or to help for their improvement. Describing the brain as an architecture of learning systems has also strong implications in Machine Learning. Our biologically informed view of pavlovian and instrumental conditioning can be very precious to revisit classical Reinforcement Learning and provide a basis to ensure really autonomous learning.

A Generic IoT Architecture for Ubiquitous Context-Aware Learning

Ubiquitous learning environments move learners out of a classroom and into the real world where learners engage in experiential and tangible learning involving instrumented physical things. Learners use peer-to-peer networks connecting learners, teachers, and a host of learning “things,” such as instrumented pieces of art, flower pots, and even buildings. A key component of such systems is wireless-enabled edge devices augmented with various types of sensors to represent the state of physical things and their context. This article presents a novel generic architecture for context-aware ubiquitous learning systems based on the Internet of Things (IoT) computing paradigm. A commonly used IoT edge device was used to implement four variants of the proposed architecture. The variants were based on advanced message queuing protocol, constrained application protocol (CoAP), message queue telemetry transport, and extensible messaging and presence protocol. All variants provided more than adequate performance for implementing ubiquitous learning scenarios. As expected, CoAP was generally more resource-efficient. However, practical differences in performance were so small that the choice of which architectural variant to use may depend on nonfunctional requirements such as reliability, security, maximum payload size, and ease of implementation.

Research and Implementation of U-Learning System Based on Experience API

Experience API provides a learner-centered model for learning data collection and learning process recording. In particular, it can record learning data from multiple data sources. Therefore, Experience API provides very good support for ubiquitous learning. In this paper, we put forward the architecture of ubiquitous learning system and the method of reading the learning record from the ubiquitous learning system. We analyze students’ learning behavior from two aspects: horizontal and vertical, and give the analysis results. The system can provide personalized suggestions for learners according to the results of learning analysis. According to the feedback from learners, we can see that this u-learning system can greatly improve learning interest and quality of learners.

Downsizing and enhancing broad learning systems by feature augmentation and residuals boosting

Recently, a broad learning system (BLS) has been theoretically and experimentally confirmed to be an efficient incremental learning system. To get rid of deep architecture, BLS shares the same architecture and learning mechanism of the well-known functional link neural networks (FLNN), but works in broad learning way on both the randomly mapped features of original features of data and their randomly generated enhancement nodes. As such, BLS often requires a huge heap of hidden nodes to achieve the prescribed or satisfactory performance, which may inevitably cause both overwhelming storage requirement and overfitting phenomenon. In this study, a stacked architecture of broad learning systems called D&BLS is proposed to achieve enhanced performance and simultaneously downsize the system architecture. By boosting the residuals between previous and current layers and simultaneously augmenting the original input space with the outputs of the previous layer as the inputs of current layer, D&BLS stacks several lightweight BLS sub-systems to guarantee stronger feature representation capability and better classification/regression performance. Three fast incremental learning algorithms of D&BLS are also developed, without the need for the whole re-training. Experimental results on some popular datasets demonstrate the effectiveness of D&BLS in the sense of both enhanced performance and reduced system architecture.

Adaptive Deep Cascade Broad Learning System and Its Application in Image Denoising

This article proposes a novel regularization deep cascade broad learning system (DCBLS) architecture, which includes one cascaded feature mapping nodes layer and one cascaded enhancement nodes layer. Then, the transformation feature representation is easily obtained by incorporating the enhancement nodes and the feature mapping nodes. Once such a representation is established, a final output layer is constructed by implementing a simple convex optimization model. Furthermore, a parallelization framework on the new method is designed to make it compatible with large-scale data. Simultaneously, an adaptive regularization parameter criterion is adopted under some conditions. Moreover, the stability and error estimate of this method are discussed and proved mathematically. The proposed method could extract sufficient available information from the raw data compared with the standard broad learning system and could achieve compellent successes in image denoising. The experiments results on benchmark datasets, including natural images as well as hyperspectral images, verify the effectiveness and superiority of the proposed method in comparison with the state-of-the-art approaches for image denoising.

A wavelet broad learning adaptive filter for forecasting and cancelling the physiological tremor in teleoperation

Physiological tremor forecasting is one of the most important issues in tele-operation that can improve the operational precision greatly. In the tele-operation, signals are three-dimensional and nonstationary time series. In this paper, a wavelet broad learning adaptive filter (WBLAF) is proposed to forecast and cancel the physiological tremor in tele-operation. Firstly, the variant architecture of original broad learning system (BLS) is proposed to extract the features of physiological tremor and the coupling information between dimensions and the novel WBLAF maps each dimensional data as the feature nodes respectively by the wavelet function. Secondly, in order to make the system can use the advantage of wavelet to extract the time-frequency features of input, a novel self-paced wavelet auto-encoder (SPWAE) is proposed to train the weights of feature mapping. Moreover, the ridge regression learning algorithm and the incremental learning of the proposed filter are applied for online learning. Finally, we designed three parts of experiments to show its effectiveness and feasibility. The first numerical experiment shows the convergence of the novel SPWAE and the second numerical experiment verifies the ability of WBLAF to predict non-stationary time series signals. In the end, semi-physical simulation experiment is implementation. As shown in the results, the novel method can effectively predict and filter out the physiological tremor in tele-operation.

Addressing Pedagogical Usability and Mobility Barriers in E-learning Systems: A Case of Youth Entrepreneurship Skill Development, Plan International Uganda

Adoption of E-learning systems to address youth self-reliance entrepreneurship skills knowledge gap in developed countries has been rapidly evolving and reached advanced stages. Nonetheless, in the developing world most especially Sub-Sahara Africa it is still infancy. Of recent, some Non-Governmental Organizations have attempted implementation of E-learning systems as alternative means to traditional elitist spaces to equip the youth with self-reliance job start-up skills. However, despite the usual challenge of ICT infrastructure development, pedagogical usability and accessibility concerns are key undermining barriers to this initiative. This study therefor focuses on design and implementation of a youth entrepreneurship skills E-learning system prototype, ported on mobile technology with an integrated informal lifelong learning model. The proposed infrastructure also accommodates a mobile learning tool that enhances access and usability concerns of out-of-school youth lifelong learners in developing countries like Uganda. System design and development was achieved through a user centred participatory design approach (UCD) in conjunction with Human access point (HAP) design technique. User requirements inform of low-fidelity card paper prototypes were later transformed into working system interfaces. The developed prototype with both web and native mobile tool simplifies the process of self-employment business start-up skills access and development using interactive videos. Similarly, the system guides a youth in selection of appropriate survival skills, and selection of the learning path based on one’s previous experience. In addition, the learning environment helps the youth to connect with skilled experts and coaches experienced in vast areas of the business. Conclusively, the platform helps survival skills providers to reach the unreached youth and monitoring of active youth ongoing projects. Evaluation of the developed system prototype was streamlined on the Unified Theory of Technology and Acceptance Model (UTAUT). From here user’s feedbacks on the developed prototype were gathered using Survey questionnaires. Study findings revealed that mobile technology can bridge youth survival entrepreneurship skills knowledge gap in developing countries. Similarly, youth survival skills provider’s responses on the developed prototype in terms of system access and usability were positive. Briefly, study findings revealed that personalized Smart mobile learning system architecture bridges pedagogical usability and mobility barriers in youth entrepreneurship skills development E-learning systems implementation in developing countries like Uganda.

The Learning Style-Based Adaptive Learning System Architecture

In the current study of learning process that show learners will take a different way and use different types of learning resources in order to learning better. Any many researchers also agree that learning materials must be able to meet the various learning styles of learners. Therefore, let learners can effective to improve their learning, for different learning styles of learners should be given different types of learning materials. In this paper the authors propose a learner's learning style-based adaptive learning system architecture that is designed to help learners advance their on-line learning along an adaptive learning path. The investigation emphasizes the relationship of learning content to the learning style of each participant in adaptive learning. An adaptive learning rule was developed to identify how learners of different learning styles may associate those contents which have the higher probability of being useful to form an optimal learning path. In this adaptive learning system architecture, it will according to different learning styles given different types of learning materials and will according to learner's profile to adjust learner's learning style for providing suitable learning materials.

Implementation of Android Mobile Terminal Learning Platform

In the mobile Internet era, in order to learn whenever and wherever possible, we design a mobile learning system based on Android platform. With the key technologies introduced, we design the overall architecture, structure layer and system flow of the mobile learning system, and further design some function modules.

Research and Design of Open and Autonomous Learning System Based on HTML5

With the rise of mobile internet applications and popularity of smartphones and tablet PC, more and more applications tend to support multi-platform access. Open and autonomous learning system is a multi-user application system which is applicable for different types of terminals. This article is based on html5 technology, integrates the current popular frameworks and builds service-oriented architecture of open and autonomous learning system. With Web Development Technology curriculum group for the case, it designs a development schema of the learning system and solves the key technical problems in system realization. This scheme can realize development and deployment once, and run in multiple terminal platform.