Slow Learning Process Research Articles

Evolutionary stochastic configuration networks for industrial data analytics

Stochastic configuration network (SCN) with compact architecture is expected for data modeling. However, the hidden-node parameters (HNPs) randomly configured may result in a slow learning process due to the redundant nodes embedded in the model. To resolve this problem, an evolutionary SCN based on an improved differential evolution (DE) algorithm is proposed in this paper. Specifically, the improved DE reuses the assignment information of last hidden node to find an appropriate search scope for the current one; employs a space reduction method to seed a promising population in the scope; and develops a performance-aware scheme to adjust the scale factor of mutation operators. The proposed evolutionary SCNs are compared with other methods on six datasets and then applied for two real-world applications. Experimental results demonstrate that the proposed method obtains superior performance in terms of compactness and accuracy, with great potential for real-world data analysis.

Human motor learning dynamics in high-dimensional tasks.

Conventional approaches to enhance movement coordination, such as providing instructions and visual feedback, are often inadequate in complex motor tasks with multiple degrees of freedom (DoFs). To effectively address coordination deficits in such complex motor systems, it becomes imperative to develop interventions grounded in a model of human motor learning; however, modeling such learning processes is challenging due to the large DoFs. In this paper, we present a computational motor learning model that leverages the concept of motor synergies to extract low-dimensional learning representations in the high-dimensional motor space and the internal model theory of motor control to capture both fast and slow motor learning processes. We establish the model's convergence properties and validate it using data from a target capture game played by human participants. We study the influence of model parameters on several motor learning trade-offs such as speed-accuracy, exploration-exploitation, satisficing, and flexibility-performance, and show that the human motor learning system tunes these parameters to optimize learning and various output performance metrics.

Remote monitoring system using slow-fast deep convolution neural network model for identifying anti-social activities in surveillance applications

Remote monitoring is the process that monitors and observes information from a distance utilizing sensors or electronic types of equipment. Remote monitoring is used in real-time applications like traffic, forest, military, shops, and hospitals to determine abnormal activities. Earlier research has done video processing methods based on computer vision techniques, but the computational complexity regarding time and memory is high. This paper designs and implements a novel Slow-Fast Convolution Neural Network (SF–CNN) to identify, detect, and classify abnormal behaviours from a surveillance video. The proposed CNN architecture learns the video frames automatically, obtains the most appropriate properties about various objects' behaviour from a large set of videos. The learning process of SF-CNN is carried out in two ways, such as slow learning and fast learning. The slow learning process is enabled when the frame rate is less, and the rapid learning process is enabled when the frame rate is high. Both the learning processes learn spatial and temporal information from the input video. Different objects, such as humans, vehicles, and animals, are detected and recognized according to their actions. All the videos have normal and abnormal activities that vary in various contexts. The proposed SF-CNN architecture provides an end-to-end solution to dealing with multiple constraints abnormal movements. The experiment is carried out on several benchmark datasets, and the performance of the SF-CNN architecture is evaluated. The proposed approach obtained 99.6% of accuracy, which is higher than the other existing techniques.

PERENCANAAN BLUEPRINT JARINGAN KOMPUTER BERBASIS INTER-VLAN ROUTING PADA SMKN 3 PUJUT

The lack of access to the internet network in schools is a factor in the slow teaching and learning process, because the lack of wireless signal coverage also causes the school community to only gather at one point. In an effort to deal with this problem, researchers built an inter-vlan network blueprint with wireless hotspots, in order to improve network performance and network security. In the process of building an inter-vlan network blueprint with a mikrotik hotspot, using the ppdioo method, among others, prepare preparation, plan plan, design design, implement, operate, optimize optimization. From the results of this study, in building a blueprint for an inter-vlan network with a mikrotik hotspot, we get the appropriate results from the initial plan. Which can improve network performance and expand access to internet network coverage.

Efficient Deep Reinforcement Learning for Optimal Path Planning

In this paper, we propose a novel deep reinforcement learning (DRL) method for optimal path planning for mobile robots using dynamic programming (DP)-based data collection. The proposed method can overcome the slow learning process and improve training data quality inherently in DRL algorithms. The main idea of our approach is as follows. First, we mapped the dynamic programming method to typical optimal path planning problems for mobile robots, and created a new efficient DP-based method to find an exact, analytical, optimal solution for the path planning problem. Then, we used high-quality training data gathered using the DP method for DRL, which greatly improves training data quality and learning efficiency. Next, we established a two-stage reinforcement learning method where, prior to the DRL, we employed extreme learning machines (ELM) to initialize the parameters of actor and critic neural networks to a near-optimal solution in order to significantly improve the learning performance. Finally, we illustrated our method using some typical path planning tasks. The experimental results show that our DRL method can converge much easier and faster than other methods. The resulting action neural network is able to successfully guide robots from any start position in the environment to the goal position while following the optimal path and avoiding collision with obstacles.

Gambaran Kesehatan Gigi Mulut dan Jumlah Streptococcus Sp pada Anak Sindroma Down di Kecamatan Patrang dan Sumbersari Jember

Down syndrome is a disorder that causes distinctive physical and mental disorders, causing a slower learning process than normal people. One of the consequences of these physical and mental limitations is the magnitude of dental and oral health problems. To check the state of oral hygiene, DMF-T and OHI-S examinations can be done. In addition, the presence of caries causes, such as Streptococcus sp can be a state of oral hygiene. This study aimed to examine the description of dental and oral health and the effect of the number of Streptococcus sp bacteria in saliva on the incidence of caries in children with Down syndrome at SLB Negeri Patrang, SLB C Taman Pendidikan and Asuhan (TPA) Sumbersari and SLB C Bintoro Regency. Jember. This study was a descriptive study with cross sectional design. This study was held at SLB Negeri Patrang, SLB C Bintoro, and SLB C TPA Sumbersari. This study showed there were differences to the caries incidence and dental hygiene of Down syndrome’s children at SLB Negeri Patrang, SLB C Bintoro, SLB C TPA Sumbersari. Besided it, there were significant differences between the DMF-T index and the colonies of Streptococcus sp, while there was no significant difference of oral hygiene down syndrome children in SLB Negeri Patrang, SLB C Bintoro, SLB C TPA Sumbersari. Briefly, this study showed that the number of Streptococcus sp colonies not frequently correlated to caries incidence and oral hygiene.

Kaitan Pandangan Aliran Filsafat Progresivisme terhadap Siswa Slow Learners dalam Proses Pembelajaran

This article contains the relationship between the views of the philosophy of progressivism towards slow learners in the learning process. Slow learners are students who have a slower learning process with an average IQ score of around 70 – 90. As a teacher, we need to pay more attention to slow learners so they can maximize their learning abilities and explore their potentials. This article uses library research methods, which conducted by using literature (library), in the form of books, notes, or other reference sources. Therefore, the philosophy of progressivism education is an understanding that makes learning meaningful and not authoritarian learning which requires students to master all the learning materials provided. There are five links between slow learners and the flow of progressivism education philosophy, as follows: (1) times, slow learners need more time to study the material given by the teacher; (2) giving problems related to everyday life, slow learners are given lessons in accordance with real examples so they are easy to imagine; (3) giving awards, awards given to slow learners can increase their motivation in learning; (4) learning with audio and visual media, which can strengthen and attract slow learners’ attention during learning process; and (5) review, reviewing learning process will strengthen their memory in remember the lessons that have been given.

Sensitivity to error during visuomotor adaptation is similarly modulated by abrupt, gradual, and random perturbation schedules

It has been suggested that sensorimotor adaptation involves at least two processes (i.e., fast and slow) that differ in retention and error sensitivity. Previous work has shown that repeated exposure to an abrupt force field perturbation results in greater error sensitivity for both the fast and slow processes. Although this implies that the faster relearning is associated with increased error sensitivity, it remains unclear what aspects of prior experience modulate error sensitivity. In the present study, we manipulated the initial training using different perturbation schedules, thought to differentially affect fast and slow learning processes based on error magnitude, and then observed what effect prior learning had on subsequent adaptation. During initial training of a visuomotor rotation task, we exposed three groups of participants to either an abrupt, a gradual, or a random perturbation schedule. During a testing session, all three groups were subsequently exposed to an abrupt perturbation schedule. Comparing the two sessions of the control group who experienced repetition of the same perturbation, we found an increased error sensitivity for both processes. We found that the error sensitivity was increased for both the fast and slow processes, with no reliable changes in the retention, for both the gradual and structural learning groups when compared to the first session of the control group. We discuss the findings in the context of how fast and slow learning processes respond to a history of errors.NEW & NOTEWORTHY We investigated what aspects of prior experience modulate error sensitivity, within the framework of a two-state model of short-term sensorimotor adaptation. We manipulated initial training on a visuomotor adaptation reaching task using specific perturbation schedules that are thought to differentially affect fast and slow learning processes, and we tested what effect these had on subsequent adaptation. We found that sensitivity to adaptation error was similarly modulated by abrupt, gradual, and random perturbation schedules.

Speech Enhancement for Hearing Impaired Based on Bandpass Filters and a Compound Deep Denoising Autoencoder

Deep neural networks have been applied for speech enhancements efficiently. However, for large variations of speech patterns and noisy environments, an individual neural network with a fixed number of hidden layers causes strong interference, which can lead to a slow learning process, poor generalisation in an unknown signal-to-noise ratio in new inputs, and some residual noise in the enhanced output. In this paper, we present a new approach for the hearing impaired based on combining two stages: (1) a set of bandpass filters that split up the signal into eight separate bands each performing a frequency analysis of the speech signal; (2) multiple deep denoising autoencoder networks, with each working for a small specific enhancement task and learning to handle a subset of the whole training set. To evaluate the performance of the approach, the hearing-aid speech perception index, the hearing aid sound quality index, and the perceptual evaluation of speech quality were used. Improvements in speech quality and intelligibility were evaluated using seven subjects of sensorineural hearing loss audiogram. We compared the performance of the proposed approach with individual denoising autoencoder networks with three and five hidden layers. The experimental results showed that the proposed approach yielded higher quality and was more intelligible compared with three and five layers.

Computational reproductions of external force field adaption without assuming desired trajectories

Optimal feedback control is an established framework that is used to characterize human movement. However, it is not fully understood how the brain computes optimal gains through interactions with the environment. In the past study, we proposed a model of motor learning that identifies a set of feedback and feedforward controllers and a state predictor of the arm musculoskeletal system to control free reaching movements. In this study, we applied the model to force field adaptation tasks where normal reaching movements are disturbed by an external force imposed on the hand. Without a priori knowledge about the arm and environment, the model was able to adapt to the force field by generating counteracting forces to overcome it in a manner similar to what is reported in the behavioral literature. The kinematics of the movements generated by our model share characteristic features of human movements observed before and after force field adaptation. In addition, we demonstrate that the structure and learning algorithm introduced in our model induced a shift in the end-point’s equilibrium position and a static force modulation, accompanied by a fast and a slow learning process. Importantly, our model does not require desired trajectories, yields movements without specifying movement duration, and predicts force generation patterns by exploring the environment. Our model demonstrates a possible mechanism through which the central nervous system may control and adapt a point-to-point reaching movement without specifying a desired trajectory by continuously updating the body’s musculoskeletal model.

An Efficient Learning Weight of Elman Neural Network with Chicken Swarm Optimization Algorithm

Data classification is one of the most frequently used tasks carried out to label information into predefined classes. The most commonly used models for data classification are Feed Forward Artificial Neural Networks (FFANN), and recurrent neural networks. The connection paths of these two structures are different from each other. Generally, the trained algorithm for these structures is back propagation (BP) algorithm which has many defects. For instance, due to the uncertain number of hidden layer neuron and fixed learning rate, it is easy to fall into local minimum, and it will never reach global minimum error function, it may stay at local minimum. Therefore, to make the slow learning process faster, it is necessary to carefully select the initial weight value. Therefore Meta heuristic search techniques play an important role for selection initial weights for the network. Chicken Swarm Optimization algorithm is one of the Meta heuristic technique effectively for selecting the initial weights values to converge to the optimal solution. However, this study proposed the Chicken Swarm Optimization for efficiently learn the initial weights value of Elman Neural Network algorithm. To validate the proposed algorithm, it is compared with existing algorithms such as Back Propagation Neural Network, Artificial Bee Colony Back Propagation and Genetic Algorithm Neural Network, and verified by two classification problems namely: IRIS and 7-bit parity. Simulation results shows that proposed algorithm outperforms with existing algorithms in terms of accuracy and mean square error.

SENTENCE PRODUCTION IN SLOW LEARNER STUDENT AND ITS IMPLICATION ON LEARNING: CASE STUDY AT SDI MELATI NUSANTARA

This study aims to know the sentence production of slow learner student and its implications for learning. The research method used in this research is descriptive qualitative method with interactive model analysis techniques. Data was collected from observation of slow learner student learning activities and interviews with the shadow teacher and the principal. The results showed that slow learner students produce simple sentences such as verbs, nouns, adjective, and prepositional sentence with declarative, imperative, interrogative types. Meanwhile, what the school did to assist slow learner student learning process is adjust the objectivesof learning, learning condition, learning materials, and learning evaluations. One example is the application of a semi-detached classroom system, the use of interactive videos as learning media, the use of companion books, and adjusting the learning evaluation systems.

Neural substrates of anticipatory motor adaptation for object lifting

Anticipatory force control is a fundamental means by which humans stave off slipping, spilling, and tilting disasters while manipulating objects. This control must often be adapted due to changes in an object’s dynamics (e.g. a lighter than expected mug of coffee) or its relation with involved effectors or digits (e.g. lift a mug with three vs. five digits). The neural processes guiding such anticipatory and adaptive control is understudied but presumably operates along multiple time scales, analogous to what has been identified with adaptation in other motor tasks, such as perturbations during reaching. Learning of anticipatory forces must be ultrafast to minimize tilting a visually symmetric object towards its concealed asymmetric center of mass (CoM), but slower when the CoM is explicitly and systematically switched from side to side. Studying the neural substrates of this latter slower learning process with rapid multiband brain imaging, in-scanner kinematics and Bayesian pattern component modelling, we show that CoM-specific pattern distances increase with repeated CoM switching exposures and improved learning. The cerebellum showed the most prominent effects, fitting with the idea that it forms a stored internal model that is used to build and update anticipatory control. CoM-specific pattern distances were present 24 h later, in line with the presence of consolidation effects.

Catecholaminergic modulation of meta-learning.

The remarkable expedience of human learning is thought to be underpinned by meta-learning, whereby slow accumulative learning processes are rapidly adjusted to the current learning environment. To date, the neurobiological implementation of meta-learning remains unclear. A burgeoning literature argues for an important role for the catecholamines dopamine and noradrenaline in meta-learning. Here, we tested the hypothesis that enhancing catecholamine function modulates the ability to optimise a meta-learning parameter (learning rate) as a function of environmental volatility. 102 participants completed a task which required learning in stable phases, where the probability of reinforcement was constant, and volatile phases, where probabilities changed every 10-30 trials. The catecholamine transporter blocker methylphenidate enhanced participants' ability to adapt learning rate: Under methylphenidate, compared with placebo, participants exhibited higher learning rates in volatile relative to stable phases. Furthermore, this effect was significant only with respect to direct learning based on the participants' own experience, there was no significant effect on inferred-value learning where stimulus values had to be inferred. These data demonstrate a causal link between catecholaminergic modulation and the adjustment of the meta-learning parameter learning rate.

The Perceived Social Context Modulates Rule Learning in Autism.

This study examines how the awareness of social situation affects rule learning in children with autism spectrum disorders (ASD) using computer-based distrust and deception games. Twenty-eight 4- to 7-year-old children with ASD and 28 age- and IQ-matched typically developing (TD) peers learned the distrusting and deceptive rules in a non-social condition, in which they were playing with a computer, or a social condition with another person pretending to interact via a computer. Results showed intact rule-learning ability in the ASDs in the non-social condition, but poorer overall performance and slower learning process than TD children when they thought that they interacted with a human opponent. Rule learning in ASD was affected by their beliefs about the social context.

SRNET: A Shallow Skip Connection Based Convolutional Neural Network Design for Resolving Singularities

Convolutional neural networks (CNNs) have shown tremendous progress and performance in recent years. Since emergence, CNNs have exhibited excellent performance in most of classification and segmentation tasks. Currently, the CNN family includes various architectures that dominate major vision-based recognition tasks. However, building a neural network (NN) by simply stacking convolution blocks inevitably limits its optimization ability and introduces overfitting and vanishing gradient problems. One of the key reasons for the aforementioned issues is network singularities, which have lately caused degenerating manifolds in the loss landscape. This situation leads to a slow learning process and lower performance. In this scenario, the skip connections turned out to be an essential unit of the CNN design to mitigate network singularities. The proposed idea of this research is to introduce skip connections in NN architecture to augment the information flow, mitigate singularities and improve performance. This research experimented with different levels of skip connections and proposed the placement strategy of these links for any CNN. To prove the proposed hypothesis, we designed an experimental CNN architecture, named as Shallow Wide ResNet or SRNet, as it uses wide residual network as a base network design. We have performed numerous experiments to assess the validity of the proposed idea. CIFAR-10 and CIFAR-100, two well-known datasets are used for training and testing CNNs. The final empirical results have shown a great many of promising outcomes in terms of performance, efficiency and reduction in network singularities issues.

Investigating the relationship between fast mapping, retention, and generalisation of words in children with autism spectrum disorder and typical development

While many studies have investigated how autism spectrum disorder (ASD) impacts how children identify the meanings of new words, this task alone does not constitute learning. Here we investigate fast (referent selection) and slow (retention, generalisation) word learning processes as an integrated system and explore relationships between these mechanisms in ASD and typical development. In Study 1, children with ASD and typically developing (TD) children matched on receptive vocabulary utilised mutual exclusivity to identify referents of unfamiliar words, but showed substantially reduced accuracy on delayed retention and generalisation trials. Thus, Study 2 investigated whether re-directing children’s attention to target objects following referent selection would enhance delayed retention. Participants received either social feedback (target objects were labelled and highlighted via social cues) or non-social feedback (target objects were labelled and highlighted via a flashing light). In both conditions, children with ASD were less accurate in their use of mutual exclusivity to fast-map novel words than TD children. However, children with ASD who received social feedback responded more accurately on delayed retention and generalisation trials than TD controls, and children with ASD who received non-social feedback or no feedback (in Study 1). Our findings imply that fundamental word learning mechanisms, and the relationships between them, are not qualitatively different in ASD. We argue that ASD may affect the efficiency of these mechanisms by disrupting children’s intake of linguistic input in natural environments, but difficulties may be mitigated by presenting visual and auditory stimuli in a way that appeals to the population’s strengths.

Both fast and slow learning processes contribute to savings following sensorimotor adaptation

Recent work suggests that the rate of learning in sensorimotor adaptation is likely not fixed, but rather can change based on previous experience. One example is savings, a commonly observed phenomenon whereby the relearning of a motor skill is faster than the initial learning. Sensorimotor adaptation is thought to be driven by sensory prediction errors, which are the result of a mismatch between predicted and actual sensory consequences. It has been proposed that during motor adaptation the generation of sensory prediction errors engages two processes (fast and slow) that differ in learning and retention rates. We tested the idea that a history of errors would influence both the fast and slow processes during savings. Participants were asked to perform the same force field adaptation task twice in succession. We found that adaptation to the force field a second time led to increases in estimated learning rates for both fast and slow processes. While it has been proposed that savings is explained by an increase in learning rate for the fast process, here we observed that the slow process also contributes to savings. Our work suggests that fast and slow adaptation processes are both responsive to a history of error and both contribute to savings. NEW & NOTEWORTHY We studied the underlying mechanisms of savings during motor adaptation. Using a two-state model to represent fast and slow processes that contribute to motor adaptation, we found that a history of error modulates performance in both processes. While previous research has attributed savings to only changes in the fast process, we demonstrated that an increase in both processes is needed to account for the measured behavioral data.

Creating A Web-Based Online Flashcard for Korean Vocabulary Memorization

The Korean language is one of the most enthused language these days, mainly caused by the Hallyu wave. In traditional language learning techniques, flashcards are often used to help memorize simple syllables. Nowadays, online flashcards are used as a replacement for the traditional flashcards, which are proven to be more effective in many ways. However, the lack of use of appropriate methods to learn a new language will result in slower learning process. Therefore, by implementing a suitable method such as the Leitner method to an online flashcard will improve the learning process. The Leitner method sets aside the questions that can be answered easily and allows users to focus on questions that cannot be recalled consistently by distributing a set of questions in five different sections. Every question that can be answered by the user is moved to the next compartment, where the question will be repeated after a specified period of time. System usability scale is used to measure the usability of the application. This application has been successfully designed and built using the Leitner method to give the user the suitable interval of time for learning with flashcards and achieved a system usability scale score of 82,5

A single exercise bout and locomotor learning after stroke: physiological, behavioural, and computational outcomes.

Previous work demonstrated an effect of a single high-intensity exercise bout coupled with motor practice on the retention of a newly acquired skilled arm movement, in both neurologically intact and impaired adults. In the present study, using behavioural and computational analyses we demonstrated that a single exercise bout, regardless of its intensity and timing, did not increase the retention of a novel locomotor task after stroke. Considering both present and previous work, we postulate that the benefits of exercise effect may depend on the type of motor learning (e.g. skill learning, sensorimotor adaptation) and/or task (e.g. arm accuracy-tracking task, walking). Acute high-intensity exercise coupled with motor practice improves the retention of motor learning in neurologically intact adults. However, whether exercise could improve the retention of locomotor learning after stroke is still unknown. Here, we investigated the effect of exercise intensity and timing on the retention of a novel locomotor learning task (i.e. split-belt treadmill walking) after stroke. Thirty-seven people post stroke participated in two sessions, 24h apart, and were allocated to active control (CON), treadmill walking (TMW), or total body exercise on a cycle ergometer (TBE). In session 1, all groups exercised for a short bout (∼5min) at low (CON) or high (TMW and TBE) intensity and before (CON and TMW) or after (TBE) the locomotor learning task. In both sessions, the locomotor learning task was to walk on a split-belt treadmill in a 2:1 speed ratio (100% and 50% fast-comfortable walking speed) for 15min. To test the effect of exercise on 24h retention, we applied behavioural and computational analyses. Behavioural data showed that neither high-intensity group showed greater 24h retention compared to CON, and computational data showed that 24h retention was attributable to a slow learning process for sensorimotor adaptation. Our findings demonstrated that acute exercise coupled with a locomotor adaptation task, regardless of its intensity and timing, does not improve retention of the novel locomotor task after stroke. We postulate that exercise effects on motor learning may be context specific (e.g. type of motor learning and/or task) and interact with the presence of genetic variant (BDNF Val66Met).