Learning System Research Articles

Boosting Adaptive Weighted Broad Learning System for Multi-Label Learning

Boosting Adaptive Weighted Broad Learning System for Multi-Label Learning

Enabling scalable and adaptive machine learning training via serverless computing on public cloud

In today’s production machine learning (ML) systems, models are continuously trained, improved, and deployed. ML design and training are becoming a continuous workflow of various tasks that have dynamic resource demands. Serverless computing is an emerging cloud paradigm that provides transparent resource management and scaling for users and has the potential to revolutionize the routine of ML design and training. However, hosting modern ML workflows on existing serverless platforms has non-trivial challenges due to their intrinsic design limitations such as stateless nature, limited communication support across function instances, and limited function execution duration. These limitations result in a lack of an overarching view and adaptation mechanism for training dynamics, and an amplification of existing problems in ML workflows.To address the above challenges, we propose SMLT, an automated, scalable and adaptive serverless framework on public cloud to enable efficient and user-centric ML design and training. SMLT employs an automated and adaptive scheduling mechanism to dynamically optimize the deployment and resource scaling for ML tasks during training. SMLT further enables user-centric ML workflow execution by supporting user-specified training deadline and budget limit. In addition, by providing an end-to-end design, SMLT solves the intrinsic problems in public cloud serverless platforms such as the communication overhead, limited function execution duration, need for repeated initialization, and also provides explicit fault tolerance for ML training. SMLT is open-sourced and compatible with all major ML frameworks. Our experimental evaluation with large, sophisticated modern ML models demonstrates that SMLT outperforms the state-of-the-art VM-based systems and existing public cloud serverless ML training frameworks in both training speed (up to 8×) and monetary cost (up to 3×).

Enhancing Learning Management Systems (LMS) Through Kano Analysis

Abstract This article explores the critical role of information and communication technology (ICT) in modern educational settings and emphasizes the importance of understanding stakeholders’ needs in the development of digital learning tools, particularly Learning Management Systems (LMS). Drawing from the Kano Model of Satisfaction, the study examines how stakeholders evaluate digital learning products and proposes a new LMS design tailored to their preferences. The paper provides insights into the acceptance and utilization of digital learning tools by stakeholders in Higher Education Institutions (HEIs), highlighting the challenges and opportunities in incorporating LMS into educational organizations. Additionally, it presents new functional factors for newly designed LMS and discusses the implications of digital learning systems in the context of modern education. Finally, the article addresses the limitations of current LMSs and underscores the importance of stakeholder acceptance for successful implementation in educational institutions.

Mitigating gradient conflicts via expert squads in multi-task learning

The foundation of multi-task learning lies in the collaboration and interaction among tasks. However, in numerous real-world scenarios, certain tasks usually necessitate distinct, specialized knowledge. The mixing of these different task-specific knowledge often results in gradient conflicts during the optimization process, posing a significant challenge in the design of effective multi-task learning systems. This study proposes a straightforward yet effective multi-task learning framework that employs groups of expert networks to decouple the learning of task-specific knowledge and mitigate such gradient conflicts. Specifically, this approach partitions the feature channels into task-specific and shared components. The task-specific subsets are processed by dedicated experts to distill specialized knowledge. The shared features are captured by a point-wise aggregation layer from the whole outputs of all experts, demonstrating superior performance in capturing inter-task interactions. By considering both task-specific knowledge and shared features, the proposed approach exhibits superior performance in multi-task learning. Extensive experiments conducted on the PASCAL-Context and NYUD-v2 datasets have demonstrated the superiority of the proposed approach compared to other state-of-the-art methods. Furthermore, a benchmark dataset for multi-task learning in underwater scenarios has been developed, encompassing object detection and underwater image enhancement tasks. Comprehensive experiments on this dataset consistently validate the effectiveness of the proposed multi-task learning strategy. The source code is available at https://github.com/chenjie04/Multi-Task-Learning-PyTorch.

Complexities of feature-based learning systems, with application to reservoir computing

This paper studies complexity measures of reservoir systems. For this purpose, a more general model that we call a feature-based learning system, which is the composition of a feature map and of a final estimator, is studied. We study complexity measures such as growth function, VC-dimension, pseudo-dimension and Rademacher complexity. On the basis of the results, we discuss how the unadjustability of reservoirs and the linearity of readouts can affect complexity measures of the reservoir systems. Furthermore, some of the results generalize or improve the existing results.

A novel four-modal nano-sensor based on two-dimensional Mxenes and fully connected artificial neural networks for the highly sensitive and rapid detection of ochratoxin A

Timely and accurate on-site detection of ochratoxin A (OTA) is extremely important for global public health. In this study, a fluorescence/colorimetric biosensor based on Ti3C2 nano-materials (Ti3C2-NMS) and a machine-learning (ML) based fluorescence/colorimetric intelligent learning system for detection of OTA concentration (COTA) were developed. The sensor was fabricated by functionalizing Ti3C2-NMS prepared by physical-exfoliation assisted metal-ion-induction using ssDNA. The Ti3C2-NMS exhibited good fluorescence quenching characteristics (FQC) and peroxidase-like activity (PLA). More surprisingly, the functionalization of Ti3C2-NMS by ssDNA further enhanced the FQC and PLA of the material, which could be used for dual-mode detection of OTA. When different COTA existed, ssDNA competitively bound to OTA, resulting in regular changes in fluorescence and colorimetric signals of the sensor, which realized the accurate and sensitive biosensing detection of OTA in two modalities. Based on a series of fluorescent/colorimetric RGB datasets collected by a self-developed application, a dual-channel ML model had been developed. This model can be integrated into mobile phones, clouds, and PCs to achieve intelligent sensing detection of OTA with the assistance of fully connected artificial neural networks. The method constructed had high specificity, low cost, and fast responsiveness, with a LOD as low as 1.58 pg mL−1, indicating excellent potential for application and promotion.

IEEE Transactions on Neural Networks and Learning Systems Information for Authors

IEEE Transactions on Neural Networks and Learning Systems Information for Authors

Penerapan Teknologi Multiplayer pada Sistem Pembelajaran Berbasis Virtual Reality

Virtual Reality is a technology that makes users seem to be interacting with their environment even though it is only a computer simulation. This research explores the application of multiplayer virtual reality (VR) technology in a learning system for assembling LAN cables. By combining elements of learning videos and accessible virtual environments together, this research aims to create a collaborative and immersive learning experience. By utilizing multiplayer technology, users can interact in real-time with fellow students, creating a collaborative and engaging learning experience. by paying attention to aspects of conceptual understanding, technical skills, and collaboration between students in a multiplayer virtual environment. This research also focuses on aspects of implementing virtual reality technology to create deeper and more immersive learning simulations. The main goal of this research is to increase interactivity and engagement of students in virtual learning environments.

Cellular-resolution optogenetics reveals attenuation-by-suppression in visual cortical neurons

The relationship between neurons' input and spiking output is central to brain computation. Studies in vitro and in anesthetized animals suggest that nonlinearities emerge in cells' input-output (IO; activation) functions as network activity increases, yet how neurons transform inputs in vivo has been unclear. Here, we characterize cortical principal neurons' activation functions in awake mice using two-photon optogenetics. We deliver fixed inputs at the soma while neurons' activity varies with sensory stimuli. We find that responses to fixed optogenetic input are nearly unchanged as neurons are excited, reflecting a linear response regime above neurons' resting point. In contrast, responses are dramatically attenuated by suppression. This attenuation is a powerful means to filter inputs arriving to suppressed cells, privileging other inputs arriving to excited neurons. These results have two major implications. First, somatic neural activation functions in vivo accord with the activation functions used in recent machine learning systems. Second, neurons' IO functions can filter sensory inputs-not only do sensory stimuli change neurons' spiking outputs, but these changes also affect responses to input, attenuating responses to some inputs while leaving others unchanged.

SecDefender: Detecting low-quality models in multidomain federated learning systems

Federated learning (FL) is an innovative distributed learning paradigm that permits multiple parties to train models collaboratively while protecting individual privacy. However, it encounters security challenges, making it vulnerable to several adversarial attacks and leading to compromising model performance. Existing research on FL poisoning attacks and defense techniques tends to be application-specific, primarily emphasizing attack capabilities. However, it fails to consider inherent vulnerabilities in FL and the impact of attack intensity. To our knowledge, no existing work has delved into these issues within a multi-domain FL environment. This paper addresses these concerns by investigating the consequences of targeted label-flipping attacks within FL systems and comprehensively examining the effects of the attacks in single-label, double-label, and triple-label scenarios with different levels of poisoning intensities. Additionally, we investigate the influence of a temporal label-flipping attack, where we study the impact of adversaries available only for specific federated training rounds. Moreover, we propose a novel server-based defense mechanism called SecDefender to detect low-quality models in both IID and non-IID settings of multi-domain environments. Our approach is rigorously evaluated against state-of-the-art alternatives using six benchmark datasets: CIC-Darknet2020, Fashion-MNIST, FEDMNIST, GTSR, HAR, and MNIST. Extensive experiments demonstrate that our proposed SecDefender significantly enhances its performance by over 65% in terms of source class recall, maintaining a low attack success rate. Consequently, there is a 1 to 2% enhancement in global model accuracy compared to existing approaches.

Broad Distributed Game Learning for intelligent classification in rolling bearing fault diagnosis

As a new Single Layer Feedforward Network (SLFN) architecture, Broad Learning System (BLS) has been widely used in the field of fault diagnosis because of its fast-training speed and high generalization capability. However, when features in different classes of signals are similar or weak, BLS generates a large number of redundant features that may be difficult to classify accurately. In view of this, a new Broad Distributed Game Learning (BDGL) method is proposed in this paper, which maps data into the game space by constructing two non-parallel game hyperplanes to achieve game and segmentation of different similar features, thereby making the data linearly differentiable in the game space. Meanwhile, a linear distribution constraint term is designed to reduce noise fitting and weak feature learning in training data learning by limiting the complexity of model parameters, thereby making the solution of the objective function simpler and faster. By comparing the Precision, Recall, F-score, Kappa and Accuracy of BDGL and the comparison methods on the two types of rolling bearing experimental data, the results show that BDGL has a high classification accuracy. In addition, the experimental results on small and noisy samples once again demonstrate the effectiveness of BDGL, which provides an efficient solution for rolling bearing fault diagnosis.

Predicting the Early Detection of Breast Cancer Using Hybrid Machine Learning Systems and Thermographic Imaging

ABSTRACTBreast cancer is a leading cause of mortality among women, emphasizing the critical need for precise early detection and prognosis. However, conventional methods often struggle to differentiate precancerous lesions or tailor treatments effectively. Thermal imaging, capturing subtle temperature variations, presents a promising avenue for non‐invasive cancer detection. While some studies explore thermography for breast cancer detection, integrating it with advanced machine learning for early diagnosis and personalized prediction remains relatively unexplored. This study proposes a novel hybrid machine learning system (HMLS) incorporating deep autoencoder techniques for automated early detection and prognostic stratification of breast cancer patients. By exploiting the temporal dynamics of thermographic data, this approach offers a more comprehensive analysis than static single‐frame approaches. Data processing involves splitting the dataset for training and testing. A predominant infrared image was selected, and matrix factorization was applied to capture temperature changes over time. Integration of convex factor analysis and bell‐curve membership function embedding for dimensionality reduction and feature extraction. The autoencoder deep neural network further reduces dimensionality. HMLS model development included feature selection and optimization of survival prediction algorithms through cross‐validation. Model performance was assessed using accuracy and F‐measure metrics. HMLS, integrating clinical data, achieved 81.6% accuracy, surpassing 77.6% using only convex‐NMF. The best classifier attained 83.2% accuracy on test data. This study demonstrates the effectiveness of thermographic imaging and HMLS for accurate early detection and personalized prediction of breast cancer. The proposed framework holds promise for enhancing patient care and potentially reducing mortality rates.

IEEE Transactions on Neural Networks and Learning Systems Publication Information

IEEE Transactions on Neural Networks and Learning Systems Publication Information

Continual compression model for online continual learning

Task-Free Continual Learning (TFCL) presents a notably demanding but realistic ongoing learning concept, aiming to address catastrophic forgetting in sequential learning systems. In this paper, we tackle catastrophic forgetting by introducing an innovative dynamic expansion framework designed to adaptively enhance the model’s capacity for novel data learning while also remembering the information learnt in the past, by using a minimal-size processing architecture. Our proposed framework incorporates three key mechanisms to mitigate model’ forgetting: (1) by employing a Maximum Mean Discrepancy (MMD)-based expansion mechanism that assesses the disparity between previously acquired knowledge and that from the new training data, serving as a signal for the model’s architecture expansion; (2) a component discarding mechanism that eliminates components characterized by redundant information, thereby optimizing the model size while fostering knowledge diversity; (3) a novel training sample selection strategy that leads to the diversity of the training data for each task. We conduct a series of TFCL experiments that demonstrate the superiority of the proposed framework over all baselines while utilizing fewer components than alternative dynamic expansion models. The results on the Split Mini ImageNet dataset, after splitting the original dataset into multiple tasks, are improved by more than 2% when compared to the closest baseline.

EFFECT OF IN-SERVICE TEACHER TRAINING PROGRAM ON TVET TRAINERS PERFORMANCE IN TECHNICAL SECONDARY SCHOOLS IN RWANDA,A CASE OF KICUKIRO DISTRICT

The general objective of this study was to examine the influence of an in-service teacher training program on the performance of TVET trainers in Kicukiro district. Specifically, this studys objectives were: to identify the level to which TVET trainer receive in-service teacher training program in Technical Secondary School Kicukiro District, to assess the level of performance of TVET trainers in Technical Secondary Schools in the Kicukiro District and to evaluate the relationship between in-service training program and TVT trainers performance in Technical Secondary School Kicukiro District. The combination learning theory and system theory was served as the researchers guide. The studys population was 207 people (197 teachers and 10 head teachers) from all of the Kicukiro Districts Technical Secondary Schools. The survey was designed in a descriptive manner. A sample size of 137 was chosen using Yamanes formula. Using random sampling and purposeful selection, the population was chosen as the sample size for the study. Every questionnaire was dispersed by the researcher. The format of the analysis was influenced by the particular qualitative and qualitative methodology employed as well as the format of the data. The data that was collected in this study was examined and displayed in the order of the research objectives utilizing frequency, percentages, and tables as descriptive statistics. Data was gathered via teacher questionnaires and head teacher interview guides. The researcher examined and interpret the results after going to the field. The study found that 84.5% of the respondents agreed that they participate in service teacher training program every time, 94% of the respondents agreed that In-service teacher training program is very necessary for all teachers, 52.5 of the respondents disagreed that In-service teacher training program is only for new teachers, 57.6% of the respondents Disagreed that In-service teacher training program is necessary for old teachers while 60.5% of the respondent disagreed that In-service teacher training program is not necessary at all. 83.8% of the respondents agreed that their participation in an in-service teacher training program has helped them improve personalities and instructional techniques in the classroom and at school, 89.7% of the respondents agreed that with the help of the in-service teacher training program, and they are able to acquire new methods of classroom management and instruction, while 94.1% of the respondents agreed that the in-service teacher training program motivates and makes teachers love their professions.The research suggests that the Ministry of Education, education planners, and stakeholders should enhance in-service training programs for TVET trainers in Kicukiro District, Rwanda, focusing on upgrading technical skills, improving instructional methodologies, establishing robust monitoring mechanisms, and investing in infrastructure.

Neuroplasticity Meets Artificial Intelligence: A Hippocampus-Inspired Approach to the Stability–Plasticity Dilemma

The stability–plasticity dilemma remains a critical challenge in developing artificial intelligence (AI) systems capable of continuous learning. This perspective paper presents a novel approach by drawing inspiration from the mammalian hippocampus–cortex system. We elucidate how this biological system’s ability to balance rapid learning with long-term memory retention can inspire novel AI architectures. Our analysis focuses on key mechanisms, including complementary learning systems and memory consolidation, with emphasis on recent discoveries about sharp-wave ripples and barrages of action potentials. We propose innovative AI designs incorporating dual learning rates, offline consolidation, and dynamic plasticity modulation. This interdisciplinary approach offers a framework for more adaptive AI systems while providing insights into biological learning. We present testable predictions and discuss potential implementations and implications of these biologically inspired principles. By bridging neuroscience and AI, our perspective aims to catalyze advancements in both fields, potentially revolutionizing AI capabilities while deepening our understanding of neural processes.

Developing a three-strand approach to playfulness in learning development

In this presentation, we outlined the development of a new Academic Skills Service at Oxford International Education Group, which, whilst supporting students from seven university-based international colleges, implemented a three-strand approach to playfulness in learning development. The presentation focused on three examples of playful approaches: The use of digital and traditional game-based learning within otherwise performance-driven classes. The creation of playful spaces in the form of extracurricular cross-college book and Maths movies clubs. The development of a gamified Moodle learning system through activity badging and levelling up. These initiatives reflect current research regarding the value of playful approaches in student learning and in counterbalancing a predominantly performance-driven higher education environment (Langan and Smart, 2018; Whitton, 2018; Forbes, 2021). Firstly, game-based learning can increase student enjoyment, motivation, and participation in the learning process and promote social belonging (James and Nerantzi, 2019; Forbes, 2021). Secondly, online book/Maths clubs can operate as playful spaces where student learning is supported in a positive social environment and creativity and risk-taking become part of the learning process (Whitton, 2018; James and Nerantzi, 2019; Forbes, 2021). Thirdly, although the gamified Moodle learning system relies on extrinsic rewards, which reflect the performance-led ethos of HE (Nørgård, Toft-Nielsen and Whitton, 2017), it can also increase student motivation in completing learning activities. This presentation proposed that learning development departments, whilst supporting student outcomes and the student experience, may encounter fewer performance-led constraints compared with programme-based delivery, enabling the utilisation of playful approaches that are otherwise underused in HE.

Investigation of the Process Parameters in Rotary Friction Welded Dissimilar AA7075/AA5083 Aluminum Alloy Joints on Fatigue Initiation using FEA and ANN

The rotary friction welding (RFW) method is one of the most widespread methods in the world for producing bimetallic components that require high mechanical strength. Simulations play a vital role in improving energy efficiency and reducing environmental impact, aligning with the sustainability goals of modern industry. A neural network (NN)-based incremental learning system was developed to predict crack growth and fatigue for AA5083 and AA7075 aluminum alloys. The results indicate the ability of this method to accommodate the input temperatures and the S-N curve and provide reliable predictions of expected fatigue. This method can reduce labor costs and time spent on crack propagation tests, enhancing the effectiveness of production processes and reducing process costs. This work also reveals the ability of neural . It maynetworks (NN) in monotonic function extrapolation like the S-N curve, which may pave the way for a wide variety of monotonic function-predicting problems. In future studies, a neural network (NN)-based increment learning scheme could be trained with random parts of individual S–N curves and applied to predict the rest. Additionally, the verification utilizing AISI 2205 and AISI 1020 steel has observed that neural networks may obtain S-N curve values for another metal with less than an 8% error rate. Friction pressure increases temperature, deformation, and stress in welding processes. Friction pressure 17 MPa increases temperature to 355 degrees Celsius, while Friction pressure 23 MPa increases deformation to 0.020 mm. A friction pressure of 29 MPa increases equivalent stress to 110 MPa. The indication of the S-N curve shows that increasing welding pressure increases Alternating Stress. Friction pressure also increases life, with minimum life cycles reaching 171040 cycles at 17 MPa, 195560 cycles at 23 MPa, and 283690 cycles at 29 MPa. Comparing research and simulation results, convergence is less than 8%, reducing error.

Application of Reservoir Computers for Chaos Synchronization and Cracking Chaos-Based Cryptography in Fermionic 2D Thirring and 4D Gursey Models with Spinor Fields

Reservoir computers have recently become one of the most widely exploited model-free approaches to chaos synchronization due to their fast convergence speed and simple yet effective learning rules. In this study, reservoir computing has been utilized to emulate the dynamics of chaotic Thirring and Gursey systems. In the supervised learning of the reservoir, one-step ahead states of the dynamical systems have been employed as the teaching signals. After the training phase, the reservoir was first run autonomously and then weakly driven by chaotic systems. It has been shown that the trained reservoir computers can exhibit the same characteristics as the attractors of the learned chaotic systems, which enable their use in synchronization tasks of chaotic systems with possible application in cracking chaos-based cryptography. To investigate the effect of noise on the performance of reservoir computers, noise signals with different colors and amplitudes have been included in the transmitted signals. The obtained results indicate that the proposed scheme can provide lower error metrics for both models.

Looking to see, listening to hear, learning to understand: centring Indigenous relationality to pursue a more-than-disciplinary academy

Indigenous scholars argue that the reciprocal relationality of life should be taken more seriously in scholarship responding to environmental crisis; however, much of orthodox academia analyses the environment as a separate category through the physical and social science disciplines. This article shows how human exceptionalism, human centrism, racial discrimination, and Euro-American centric privilege interweave in academic institutions and practices, often invisibly and thus insidiously, to dismiss Indigenous scholarship and lived experiences. We explore how to meaningfully address these problematics, presenting examples from our academic practices to overturn colonial and imperial privilege. We present a more-than-disciplinary approach to undiscipline nature, knowledge, and peoples. We argue that this paradigm shift is best led by Indigenous leaders in relationality, whose expert knowledge inheritance already holds nature with society. We present an Indigenous, pedological approach, to help destabilise orthodox academic disciplinary approaches and organisational structures through foregrounding Indigenous learning and knowledge systems.