Learning Problems Research Articles

Liraglutide alleviated alpha-pyrrolidinovalerophenone (α-PVP) induced cognitive deficits in rats by modifying brain mitochondrial impairment

The use of NPS compounds is increasing, and impairment in spatial learning and memory is a growing concern. Alpha-pyrrolidinovalerophenone (α-PVP) consumption, as a commonly used NPS, can impair spatial learning and memory via the brain mitochondrial dysfunction mechanism. Liraglutide isone of the most well-known Glucagon-Like Peptide 1 (GLP-1) agonists that is used as an anti-diabetic and anti-obesity drug. According to current research, Liraglutide likely ameliorates cognitive impairment in neurodegenerative conditions and substance use disorders. Hence, the purpose of this study is examining the effect of Liraglutide on α-PVP-induced spatial learning and memory problems due to brain mitochondrial dysfunction. Wistar rats (8 in each group) received α-PVP (20 mg/kg/d for 10 consecutive days, intraperitoneally (I.P.)). Then, Liraglutide was administered at 47 and 94 μg/kg/d, I.P., for 4 weeks following the α-PVP administration. The Morris Water Maze (MWM) task evaluated spatial learning and memory 24 h after Liraglutide treatment. Bedside, brain mitochondrial activity parameters, including reactive oxygen species (ROS) level, mitochondrial membrane potential (MMP), cytochrome c release, mitochondrial outer membrane damage and swelling, and brain ADP/ATP ratio, were studied. Our results showed that Liraglutide ameliorated α-PVP-induced spatial learning and memory impairments through alleviating brain mitochondrial dysfunction (which is indicated by increasing ROS formation, collapsed MMP, mitochondrial outer membrane damage, cytochrome c release, mitochondrial swelling, and increased brain ADP/ATP ratio). This study could be used as a starting point for future studies about the possible role of Liraglutide in ameliorating mitochondrial dysfunction leading to substance use disorder- induced cognitive impairment.

Building Inspirational Islamic Education Learning with the Flipped Learning Model using Canva Application as a Solution to 21st Century Islamic Education Learning Problems at SD Al Falah Surabaya

Building Inspirational Islamic Education Learning with the Flipped Learning Model using Canva Application as a Solution to 21st Century Islamic Education Learning Problems at SD Al Falah Surabaya

Bond Strength Evaluation of FRP–Concrete Interfaces Affected by Hygrothermal and Salt Attack Using Improved Meta-Learning Neural Network

Fiber-reinforced polymer (FRP) laminates are popular in the strengthening of concrete structures, but the durability of the strengthened structures is of great concern. Due to the susceptibility of the epoxy resin used for bonding and the deterioration of materials, the bond performance of the FRP–concrete interface could be degraded due to environmental exposure. This paper aimed to establish a data-driven method for bond strength prediction using existing test results. Therefore, a method composed of a Back Prorogation Net (BPNN) and Meta-learning Net was proposed, which can be used to solve the implicit regression problems in few-shot learning and can obtain the deteriorated bond strength and the impact weight of each parameter. First, the pretraining database Meta1, a database of material strength degradation, was established from the existing results and used in the meta-learning network. Then, the database Meta2 was built and used in the meta-learning network for model fine-tuning. Finally, combining all prior knowledge, not only the degradation of the FRP–concrete bond’s strength was predicted, but the respective weights of the environment parameters were also obtained. This method can accurately predict the degradation of the bond performance of FRP–concrete interfaces in complex environments, thus facilitating the further assessment of the remaining service life of FRP-reinforced structures.

PKM IMPLEMENTASI DIGITAL LITERACY DALAM PEMBELAJARAN BAHASA INGGRIS PADA SISWA SMK DI KABUPATEN SEMARANG

The Implementation of Digital Literacy in English Language Learning for Students of SMK Muhammadiyah Ungaran Semarang Regency is carried out to increase students' interest in learning, especially in English lessons by applying digital literacy, especially Kahoot. The background of this activity is to overcome some problems in English learning, for example: lack of references and students' low interest in learning English. Learning methods that are less developed and carried out rigidly can cause boredom for students. Generating interest in learning English is one of the tasks and challenges for English teachers. The implementation of the activity begins with socialization to the students of Universitas Ngudi Waluyo who are involved in this activity, followed by coordination with the target school regarding the implementation schedule. It ends with reporting the results of activities after the implementation of the program is complete. This program provides benefits not only for the students but also for the teachers at SMK Muhammadiyah Ungaran. The English teachers get new insight for teaching method by using digital application. This method is proven interested for the students and help them to understand English more easily Keywords: Digital Literacy, English language learning

Improved classification of soil As contamination at continental scale: Resolving class imbalances using machine learning approach

The identification of arsenic (As)-contaminated areas is an important prerequisite for soil management and reclamation. Although previous studies have attempted to identify soil As contamination via machine learning (ML) methods combined with soil spectroscopy, they have ignored the rarity of As-contaminated soil samples, leading to an imbalanced learning problem. A novel ML framework was thus designed herein to solve the imbalance issue in identifying soil As contamination from soil visible and near-infrared spectra. Spectral preprocessing, imbalanced dataset resampling, and model comparisons were combined in the ML framework, and the optimal combination was selected based on the recall. In addition, Bayesian optimization was used to tune the model hyperparameters. The optimized model achieved recall, area under the curve, and balanced accuracy values of 0.83, 0.88, and 0.79, respectively, on the testing set. The recall was further improved to 0.87 with the threshold adjustment, indicating the model's excellent performance and generalization capability in classifying As-contaminated soil samples. The optimal model was applied to a global soil spectral dataset to predict areas at a high risk of soil As contamination on a global scale. The ML framework established in this study represents a milestone in the classification of soil As contamination and can serve as a valuable reference for contamination management in soil science.

A family of spectral conjugate gradient methods with strong convergence and its applications in image restoration and machine learning

In this paper, we propose a family of spectral conjugate gradient methods for solving unconstrained optimization problems. Specifically, we provide two classes of bounded spectral parameters to be chosen, design a new truncation scheme of the non-negative conjugate parameter and set a restart procedure in our search direction. Independently of the specific spectral parameter, conjugate parameter and line search criterion, we prove that our proposed family satisfies the sufficient descent condition. We also prove its strong convergence under mild assumptions and the weak Wolfe line search. Numerical comparisons with other methods demonstrate the outstanding performances of our algorithm for solving medium–large-scale unconstrained optimization, image restoration and machine learning problems.

Analysis of the Gas Safety Situation Based on the Schweizer-Sklar Rule.

In the coal mining process, a large amount of harmful gases will be produced, which we call "gas". The main component of gas is methane. After the methane concentration reaches a certain limit, an explosion will occur, seriously affecting the safety of the production of coal mines. Gas safety situational awareness is an important basis for gas early warning. In order to take appropriate measures for different levels of risk, the safety situation level is classified into five levels, which can be attributed to the classification problem in machine learning. We propose a gas security situation analysis model based on the Schweizer-Sklar rule (GSSAM-SSR) using the multiweighted Schweizer-Sklar triangular norm array combination rule (SSR). First, the raw data are preprocessed. The model uses K-means clustering with feature preference to determine the label column of the data set and principal component analysis to reduce the dimensionality of the collected high-dimensional gas-related data. Ten basic classification models are constructed, and the best performing models are selected as the basic classifier set by using the preorder method. Then, the SSR combination rule is proposed to combine the basic classifiers. Finally, considering the dynamic and quasi dynamic data of the mine, the basic classifier set and SSR combination rule are combined to construct the GSSAM-SSR model and applied to the gas security situation analysis. Experimental results show that the SSR combination rule achieves the best classification performance, exhibiting an accuracy of 94.43%, which is 2.51% higher than the accuracy of the gradient boosting decision tree model, which was the highest performing basic classifier and higher than the accuracies of the voting method and Bayesian combination rule.

Self-paced method for transfer partial label learning

In partial label learning (PLL) problem, each training sample corresponds to a group of candidate labels, in which only one label is the ground-truth label (correct label). Almost all the existing PLL algorithms attempt to eliminate the ambiguity of the candidate label sets by treating all labels indiscriminately. However, this kind of approach lacks the consideration of the complexity between the labels and the instances in the training process. Encouraged by the extensive researches of the self-paced learning (SPL) and transfer learning (TL) in various fields, this paper introduces SPL and TL together to address the PLL problem and proposes a new SPL framework, which is called self-paced method for transfer partial label learning (SPTPLL). The proposed model utilizes transfer learning model to share the parameters and regularization terms of the Support Vector Machine (SVM), which can transfer knowledge from the source task to the target task. Additionally, we implement the self-paced learning scheme by choosing a suitable self-paced function to enhance the robustness of the proposed model. In the process of learning iteration, the priority of training examples with their candidate labels is ranked through self-paced learning to control the learning process. Finally, we demonstrate the superior performance of the proposed method through a large number of experiments compared with state-of-the-art baseline methods.

Persepsi Siswa Terhadap Problematika dalam Pembelajaran Ips Kelas VIII SMP Negeri 1 Ngunut Tulungagung

Frequent emergence of problems faced by students in social studies learning activities problems experienced by students caused by many factors, both internal and external factors. Social studies learning at the junior secondary level is given or presented in an integrated manner to be more easily understood and learned by students. Because social studies is an integration of several branches of social sciences are simplified. The focus of research in writing this thesis is: (1) What are the problems of students in learning social studies class VIII SMP Negeri 1 Ngunut Tulungagung?, (2) What causes the emergence of problems experienced by students in learning social studies class VIII Smp Negeri 1 Ngunut Tulungagung?, (3)How can teachers overcome the problems experienced by students in social studies class VIII of Smp Negeri 1 Ngunut Tulungagung?. The objectives of the study are: (1) to describe the problems in social learning experienced by students of Grade VIII SMP Negeri 1 Ngunut Tulungagung, (2) to describe the causes of the emergence of problems in social learning experienced by students of Grade VIII SMP Negeri 1 Ngunut Tulungagung, (3) to describe how teachers overcome problems in social learning experienced by students of Grade VIII SMP Negeri 1 Ngunut Tulungagung. This research is categorized in qualitative research. with data collection techniques conducted by observation, interviews, and documentation with the research site at SMP Negeri 1 Ngunut Tulungagung. The subjects in this study were social studies teachers and eighth grade students of SMP Negeri 1 Ngunut Tulungagung. The results showed that: (1) problems experienced by grade VIII students in social studies, namely students who have minimal or less reading interest in social studies subjects. Availability of package books and worksheets for students is limited. The methods and learning media used by class teachers are less varied. The teacher is not in control of the class. (2) the cause of students experiencing problems in social science learning is that students do not have books and worksheets as companion books in social studies learning. Students are allowed to bring cellphones to school. The teacher's voice is less loud and clear when teaching. Many students are crowded during the class learning process. (3) how teachers overcome Student Problems in social studies learning is class management. Variety of learning (methods, media, and tasks). The teacher's attitude is firm. Outdoor learning.

Statistical physics of principal minors: Cavity approach.

Determinants are useful to represent the state of an interacting system of (effectively) repulsive and independent elements, like fermions in a quantum system and training samples in a learning problem. A computationally challenging problem is to compute the sum of powers of principal minors of a matrix which is relevant to the study of critical behaviors in quantum fermionic systems and finding a subset of maximally informative training data for a learning algorithm. Specifically, principal minors of positive square matrices can be considered as statistical weights of a random point process on the set of the matrix indices. The probability of each subset of the indices is in general proportional to a positive power of the determinant of the associated submatrix. We use Gaussian representation of the determinants for symmetric and positive matrices to estimate the partition function (or free energy) and the entropy of principal minors within the Bethe approximation. The results are expected to be asymptotically exact for diagonally dominant matrices with locally treelike structures. We consider the Laplacian matrix of random regular graphs of degree K=2,3,4 and exactly characterize the structure of the relevant minors in a mean-field model of such matrices. No (finite-temperature) phase transition is observed in this class of diagonally dominant matrices by increasing the positive power of the principal minors, which here plays the role of an inverse temperature.

Robust Structure-Aware Graph-based Semi-Supervised Learning: Batch and Recursive Processing

Graph-based semi-supervised learning plays an important role in large scale image classification tasks. However, the problem becomes very challenging in the presence of noisy labels and outliers. Moreover, traditional robust semi-supervised learning solutions suffers from prohibitive computational burdens thus cannot be computed for streaming data. Motivated by that, we present a novel unified framework robust structure-aware semi-supervised learning called Unified RSSL (URSSL) for batch processing and recursive processing robust to both outliers and noisy labels. Particularly, URSSL applies joint semi-supervised dimensionality reduction with robust estimators and network sparse regularization simultaneously on the graph Laplacian matrix iteratively to preserve the intrinsic graph structure and ensure robustness to the compound noise. First, in order to relieve the influence from outliers, a novel semi-supervised robust dimensionality reduction is applied relying on robust estimators to suppress outliers. Meanwhile, to tackle noisy labels, the denoised graph similarity information is encoded into the network regularization. Moreover, by identifying strong relevance of dimensionality reduction and network regularization in the context of robust semi-supervised learning (RSSL), a two-step alternative optimization is derived to compute optimal solutions with guaranteed convergence. We further derive our framework to adapt to large scale semi-supervised learning particularly suitable for large scale image classification and demonstrate the model robustness under different adversarial attacks. For recursive processing, we rely on reparameterization to transform the formulation to unlock the challenging problem of robust streaming-based semi-supervised learning. Last but not least, we extend our solution into distributed solutions to resolve the challenging issue of distributed robust semi-supervised learning when images are captured by multiple cameras at different locations. Extensive experimental results demonstrate the promising performance of this framework when applied to multiple benchmark datasets with respect to state-of-the-art approaches for important applications in the areas of image classification and spam data analysis.

Identifying HR Management and Supervision Activities at SMAN 1 Merangin to Face Society 5.0 and Industrial Revolution 4.0

The article explains the importance of managing and supervising human resources, especially teachers, in Society 5.0 and the era of Industrial Revolution 4.0, which is full of knowledge of technology and digital-based activities. The principal and the driving/developing teachers did this management and supervision effort. The author observed this, with the school being the main focus, SMAN 1 Merangin. This article uses a qualitative research method by prioritising the process of communication interaction and observation in research and a systematic literature review (SLR) method by collecting other sources regarding HR management and supervision in facing society 5.0 and the Industrial Revolution 4.0 from books, journals and other sources. The challenge faced by SMAN 1 Merangin based on the interview is how to get all teachers, or at least 50% of the teachers at SMAN 1 Merangin, to get a driving teacher certificate and other training certificates so that they can get out of the safe zone and become proficient in information technology. While facing this era, SMAN 1 Merangin teachers adept at technology and good at solving learning problems are appointed to drive teachers and supervise other teachers in collaboration with the school principal to improve performance and catch up with the times.

MARLUI: Multi-Agent Reinforcement Learning for Adaptive Point-and-Click UIs

As the number of selectable items increases, point-and-click interfaces rapidly become complex, leading to a decrease in usability. Adaptive user interfaces can reduce this complexity by automatically adjusting an interface to only display the most relevant items. A core challenge for developing adaptive interfaces is to infer user intent and chose adaptations accordingly. Current methods rely on tediously hand-crafted rules or carefully collected user data. Furthermore, heuristics need to be recrafted and data regathered for every new task and interface. To address this issue, we formulate interface adaptation as a multi-agent reinforcement learning problem. Our approach learns adaptation policies without relying on heuristics or real user data, facilitating the development of adaptive interfaces across various tasks with minimal adjustments needed. In our formulation, a user agent mimics a real user and learns to interact with an interface via point-and-click actions. Simultaneously, an interface agent learns interface adaptations, to maximize the user agent's efficiency, by observing the user agent's behavior. For our evaluation, we substituted the simulated user agent with actual users. Our study involved twelve participants and concentrated on automatic toolbar item assignment. The results show that the policies we developed in simulation effectively apply to real users. These users were able to complete tasks with fewer actions and in similar times compared to methods trained with real data. Additionally, we demonstrated our method's efficiency and generalizability across four different interfaces and tasks.

A Study on the Prediction Method of English Performance in Universities based on the Stacking Integrated Model

Students’ performance in higher education reflects their overall quality in higher education. By predicting the performance, students with greater learning problems can be screened out early and given appropriate guidance. To predict students’ performance in English, the knowledge information of courses, examination papers, and historical examination records are used to build a feature project of students’ examinations. Meanwhile, the features strongly correlated with their performance are filtered out. Then the next step of performance prediction is carried out. The results showed that a neural network long and short-term memory performance prediction model incorporating an attention mechanism was more effective than other models in predicting English performance in higher education. Further experiments found that the model reduced the error by 1.04% on the MAE metric, 0.53% on the RMSE metric, and increased its value by 4.12% on the R2 metric. Adding the new feature dataset led to better forecasting by the Att-LSTM model in all metrics. This indicated that the enhanced dataset temporality could improve the effectiveness of the Att-LSTM model in predicting English grades in higher education. The stacked integrated prediction model, by integrating multiple strong regressors, can avoid poor prediction and excessive overall bias due to one regressor and increase the soundness and prediction precision of the mode.

Transfer Learning in Machine Learning: A Review of Methods and Applications

Transfer learning has gained significant traction and popularity in the field of machine learning due to its wide range of potential applications. This review article offers a thorough examination of transfer learning techniques and their wide-ranging applications in several fields. This text provides a thorough evaluation of the literature, focusing on important research and the methodology used. Furthermore, a comparative table highlighting transfer learning research across several areas provides valuable insights into the wide range of applications. The inclusion criteria were centred on recent articles published within the past five years that comprehensively examined transfer learning methodologies, applications, frameworks, problems, and future directions. The review articles highlight the widespread use of transfer learning models, the effectiveness of data augmentation strategies, and the capability of transfer learning to tackle issues particular to different domains. Nevertheless, some constraints like as biases in the dataset, difficulties in interpreting the model, and problems with scalability have been recognised. These limitations provide opportunities for future research to focus on creating transfer learning algorithms that are more resilient and easier to read.

Analisis Permasalahan Dalam Pembelajaran IPA Sekolah Dasar Swasta Attaufiq Medan

This research aims to find out the problems found in science learning at SDS AT-TAUFIQ Medan. The method used in this research is a qualitative method with a descriptive approach. Data was obtained through interviews. The results of this research show that there are still many problems related to science learning at Attaufiq Medan Private Elementary School. The basic problem is the lack of motivation and enthusiasm of students in the learning process. The lack of facilities and infrastructure is also a problem in science learning at SDS At-Taufiq Medan. By making innovations such as applying learning media, you can create a comfortable and enjoyable learning atmosphere.

Enhancing active noise control through stacked autoencoders: Training strategies, comparative analysis, and evaluation with practical setup

This work is motivated by the imperative to overcome the limitations of conventional active noise control (ANC) techniques. These limitations are rooted in linear systems like the least mean square algorithm when faced with nonlinear distortions in the acoustic environment and system electronics, such as loudspeakers. This work addresses the nonlinear ANC problem by conceptualizing ANC as a deep learning problem. A novel stacked autoencoder (SAE) model is proposed, which is trained to estimate the noise that should be played at a loudspeaker so that the noise received at an error microphone may be attenuated. ANC experiments are initially set up in an anechoic room to study the fundamental performance characteristics of the architecture without extraneous considerations of room impulse response. This is followed by performance analysis in a normal reflective room. Actual noise, comprising broadband and tonal noise, is recorded in both an anechoic room and a normal reflective room. The importance of these experiments in the design process lies in their ability to account for nonlinear effects in actual setups, surpassing the limitations of relying solely on models. Experiment outcomes demonstrate that the proposed SAE model yields a noise reduction (NR) of up to 26.44 dB for wideband noise and up to 35.74 dB for tonal noise types. Additionally, there is an improvement in the extent of the quiet zone provided by the proposed SAE for tonal noise cases as compared to the traditional filtered-x least mean square (FxLMS) technique.

Linear discriminant analysis with trimmed and difference distribution modeling

Linear discriminant analysis (LDA) has been widely used to extract features to solve various machine learning problems. However, the current LDA methods can be confused by highly dispersed samples. To address this problem, we develop a new LDA method based on a new expression of the theoretical error in classification. The method first combines different class distributions and then constructs the projection matrix on the basis of the statistics of the combined distribution. This is very different from conventional methods that estimate the statistics from individual classes first. The statistics are then combined to construct the projection matrix. The merit of the proposed approach is that it selects discriminant pairs of the combined distribution and discards the scatter structure caused by the dispersed samples. The statistics of this trimmed distribution are less sensitive to the dispersed samples and produce a robust projection matrix. The pairs are selected by a trimming model. The solution is obtained by the alternating direction method of multipliers (ADMM). The proposed LDA method is compared with 17 other LDA methods over 13 datasets belonging to the UC Irvine (UCI) repository, the knowledge extraction based on evolutionary learning (KEEL) repository, and publicly available image websites with different numbers of training samples. The experimental results indicate that the proposed method outperforms the existing methods in many cases.

On data efficiency of univariate time series anomaly detection models

In machine learning (ML) problems, it is widely believed that more training samples lead to improved predictive accuracy but incur higher computational costs. Consequently, achieving better data efficiency, that is, the trade-off between the size of the training set and the accuracy of the output model, becomes a key problem in ML applications. In this research, we systematically investigate the data efficiency of Univariate Time Series Anomaly Detection (UTS-AD) models. We first experimentally examine the performance of nine popular UTS-AD algorithms as a function of the training sample size on several benchmark datasets. Our findings confirm that most algorithms become more accurate when more training samples are used, whereas the marginal gain for adding more samples gradually decreases. Based on the above observations, we propose a novel framework called FastUTS-AD that achieves improved data efficiency and reduced computational overhead compared to existing UTS-AD models with little loss of accuracy. Specifically, FastUTS-AD is compatible with different UTS-AD models, utilizing a sampling- and scaling law-based heuristic method to automatically determine the number of training samples a UTS-AD model needs to achieve predictive performance close to that when all samples in the training set are used. Comprehensive experimental results show that, for the nine popular UTS-AD algorithms tested, FastUTS-AD reduces the number of training samples and the training time by 91.09–91.49% and 93.49–93.82% on average without significant decreases in accuracy.

Low-resolution few-shot learning via multi-space knowledge distillation

Existing few-shot classification models usually rely on limited known support images to form class centers, and classify query images based on the distance between their embedding and the class centers. However, these models assume that the query image is high-resolution (HR), and thus suffer from significant performance degradation when applied to low-resolution (LR) images. Due to the lack of discriminative information in LR images, there is a noticeable discrepancy between the embeddings of LR query images and the class centers formed by HR support images. To address this issue, we first formulate the problem of Low-Resolution Few-Shot Learning (LRFSL), where the support images are HR while the query images are only available in LR. Then, we propose an end-to-end pipeline that leverages mutual learning between a super-resolution (SR) network and a few-shot classification network. To further reduce the domain discrepancy between the embeddings of the SR images and HR class centers, we introduce a multi-space knowledge distillation strategy that aims to transfer pixel-level, feature-level, and logit-level knowledge of the HR domain to the SR domain. We conduct extensive experiments on classic few-shot datasets: miniImageNet, tieredImageNet, and the fine-grained few-shot dataset CUB. Experimental results show that our method can handle few-shot classification with LR input, and achieve performance that is almost comparable to using HR images as input. Specifically, our method achieves an average accuracy improvement of 26.47% with the Meta-Baseline Model and 7.44% with the Meta DeepBDC Model across all datasets compared to LR Query.