Category Learning Research Articles

Learning semantic categories of L2 verbs: The case of cutting and breaking verbs.

To attain native-like proficiency in second-language word usage, learners have to discover intricate semantic categories in the target language. We investigated the factors influencing the development of two aspects of second-language learners' semantic categories: the category center and category boundary of word meanings. In the experiment, second-language learners of Japanese, whose first language is Mandarin, were asked to produce the best verb for 28 videos depicting various cutting and breaking events. Descriptive analyses were conducted to compare the verb patterns used by second-language learners with those of native speakers. The second-language learners' verb use pattern suggested their struggle in delineating the semantic ranges of breaking verbs in a native-like manner. Model analyses further revealed that different factors contribute to learning two different aspects of word meanings. The learning category center of word meaning depended on the similarity between the lexical domains in the first and second languages. On the contrary, the success of learning the semantic boundaries of verbs required a large input frequency and smaller semantic coverage, and smaller category ambiguity. The results suggest that constructing a semantic domain in the second language should be evaluated from at least two different aspects of semantic representation.

What is moving where? Infants' visual attention to dynamic objects may assist with processing of spatial relations.

A central question in infant spatial cognition concerns how infants form abstract categories of spatial relations such as support (on) and containment (in). Prior work suggests two different possibilities regarding the role of attention to objects in infants' formation of abstract categories of spatial relations: Attention to objects may compete with (and thus hamper) attention to the spatial relations between them, or assist with encoding of the spatial relation information. Using eye-tracking, we examined how infants' visual attention to objects related to their successful formation of an abstract category of support relations (i.e., an object on another). Thirty-eight 8-month-old infants' eye movements were recorded during a support categorization task, where infants were habituated to four dynamic events depicting support relations (e.g., resting a block on a box) and then presented with test events that depicted either a support or containment relation with objects that they had seen or not seen in the habituation phase. Based on their looking time to the familiar versus novel spatial relation in the test, infants were classified into two groups: categorizers, who formed an abstract category of a support relation, and non-categorizers, who did not do so. During their initial phase of learning (i.e., the first habituation trial), categorizers paid greater attention to the object moved by a hand (i.e., the dynamic object) in comparison to non-categorizers, whereas their attention to the static object or their gaze shifts between the two objects did not differ. In addition, when presented with novel objects in a novel spatial relation after habituation, only categorizers displayed asymmetric attention between the objects, attending to the dynamic object more than the static object. Gaze shifts and attention to the concave area (i.e., hole) of the container did not differ between categorizers and non-categorizers. These findings suggest that infants' focused attention to an object in motion may play a key role in young infants' spatial category learning, and support the idea that attention to objects can assist with encoding of the spatial relational information.

High-variability training does not enhance generalization in the prototype-distortion paradigm.

Classic studies of human categorization learning provided evidence that high-variability training in the prototype-distortion paradigm enhances subsequent generalization to novel test patterns from the learned categories. More recent work suggests, however, that when the number of training trials is equated across low-variability and high-variability training conditions, it is low-variability training that yields better generalization performance. Whereas the recent studies used cartoon-animal stimuli varying along binary-valued dimensions, in the present work we return to the use of prototype-distorted dot-pattern stimuli that had been used in the original classic studies. In accord with the recent findings, we observe that high-variability training does not enhance generalization in the dot-pattern prototype-distortion paradigm when the total number of training trials is equated across the conditions, even when training with very large numbers of distinct instances. A baseline version of an exemplar model captures the major qualitative pattern of results in the experiment, as do prototype models that make allowance for changes in parameter settings across the different training conditions. Based on the modeling results, we hypothesize that although high-variability training does not enhance generalization in the prototype-distortion paradigm, it may do so when participants learn more complex category structures.

Advancements in microneedle fabrication techniques: artificial intelligence assisted 3D-printing technology.

Microneedles (MNs) are micron-scale needles that are a painless alternative to injections for delivering drugs through the skin. MNs find applications as biosensing devices and could serve as real-time diagnosis tools. There have been numerous fabrication techniques employed for producing quality MN-based systems, prominent among them is the three-dimensional (3D) printing. 3D printing enables the production of quality MNs of tuneable characteristics using a variety of materials. Further, the possible integration of artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL) with 3D printing makes it an indispensable tool for fabricating microneedles. Provided that these AI tools can be trained and act with minimal human intervention to control the quality of products produced, there is also a possibility of mass production of MNs using these tools in the future. This work reviews the specific role of AI in the 3D printing of MN-based devices discussing the use of AI in predicting drug release patterns, its role as a quality control tool, and in predicting the biomarker levels. Additionally, the autonomous 3D printing of microneedles using an integrated system of the internet of things (IoT) and machine learning (ML) is discussed in brief. Different categories of machine learning including supervised learning, semi-supervised learning, unsupervised learning, and reinforced learning have been discussed in brief. Lastly, a brief section is dedicated to the biosensing applications of MN-based devices.

Testimony and observation of statistical evidence interact in adults' and children's category-based induction

Hearing generic or other kind-relevant claims can influence the use of information from direct observations in category learning. In the current study, we ask how both adults and children integrate their observations with testimony when learning about the causal property of a novel category. Participants were randomly assigned to hear one of four types of testimony: generic, quantified “all”, specific, or only labels. In Study 1, adults (N = 1249) then observed that some proportion of objects (10%–100%) possessed a causal property. In Study 2, children (N = 123, Mage = 5.06 years, SD = 0.61 years, range 4.01–5.99 years) observed a sample where 30% of the objects had the causal property. Generic and quantified “all” claims led both adults and children to generalize the causal property beyond what was observed. Adults and children diverged, however, in their overall trust in testimony that could be verified by observations: adults were more skeptical of inaccurate quantified claims, whereas children were more accepting. Additional memory probes suggest that children's trust in unverified claims may have been due to misremembering what they saw in favor of what they heard. The current findings demonstrate that both child and adult learners integrate information from both sources, offering insights into the mechanisms by which language frames first-hand experience.

The Pigeon as a Model of Complex Visual Processing and Category Learning.

Over the past 30 years, behavioral, computational, and neuroscientific investigations have yielded fresh insights into how pigeons adapt to the diverse complexities of their visual world. A prime area of interest has been how pigeons categorize the innumerable individual stimuli they encounter. Most studies involve either photorealistic representations of actual objects thus affording the virtue of being naturalistic, or highly artificial stimuli thus affording the virtue of being experimentally manipulable. Together those studies have revealed the pigeon to be a prodigious classifier of both naturalistic and artificial visual stimuli. In each case, new computational models suggest that elementary associative learning lies at the root of the pigeon's category learning and generalization. In addition, ongoing computational and neuroscientific investigations suggest how naturalistic and artificial stimuli may be processed along the pigeon's visual pathway. Given the pigeon's availability and affordability, there are compelling reasons for this animal model to gain increasing prominence in contemporary neuroscientific research.

Scene Recognition With Objectness, Attribute, and Category Learning

Scene Recognition With Objectness, Attribute, and Category Learning

Integrating multiscale and machine learning approaches towards the SAMPL9 log P challenge.

The partition coefficient (log P) is an important physicochemical property that provides information regarding a molecule's pharmacokinetics, toxicity, and bioavailability. Methods to accurately predict the partition coefficient have the potential to accelerate drug design. In an effort to test current methods and explore new computational techniques, the statistical assessment of the modeling of proteins and ligands (SAMPL) has established a blind prediction challenge. The ninth iteration challenge was to predict the toluene-water partition coefficient (log Ptol/w) of sixteen drug molecules. Herein, three approaches are reported broadly under the categories of quantum mechanics (QM), molecular mechanics (MM), and data-driven machine learning (ML). The three blind submissions yield mean unsigned errors (MUE) ranging from 1.53-2.93 log Ptol/w units. The MUEs were reduced to 1.00 log Ptol/w for the QM methods. While MM and ML methods outperformed DFT approaches for challenge molecules with fewer rotational degrees of freedom, they suffered for the larger molecules in this dataset. Overall, DFT functionals paired with a triple-ζ basis set were the simplest and most effective tool to obtain quantitatively accurate partition coefficients.

Hyping the Students for Schoolwork: Incorporating Brain-Based Learning in Intermediate ESP Classes

This study was conducted to examine the impact of Brain-Based Learning (BBL) considerations to the academic performance of the Grade 4 and 6 pupils in Edukasyon sa Pagpapakatao (ESP) at Fourth Estate Elementary School BF Homes. Brain-based learning posits a framework that categorizes orchestrated immersion, relaxed alertness, and active listening. The effects of BBL were gauged through summative tests, performance tasks, and periodical exams. Total enumeration was utilized in determining the respondents and 595 intermediate pupils participated. The study administered a validated instrument using Caine and Caine’s triadic BBL framework. Findings reveal that the threefold categories of brain-based learning have positive effects on all the three aspects of class performance at different levels.

Research Changes in Curriculum and Pedagogy Based on Big Data Perspectives

Abstract Analyzing the research changes in curriculum and pedagogy is significant in exploring its developmental vein and future direction in China, and helps establish a modern academic framework. In this paper, the KNN algorithm is used to meticulously process the data related to the reform of curriculum and pedagogy, and the factor analysis model is used to explore the influencing factors behind these data and the strength of their effects. The clustering and integration method is further used to categorize and integrate these factors to explore the development trend of curriculum and pedagogy deeply. By analyzing the changes in research hotspots, research directions, and author influence between 2010 and 2022, it was found that between 2010 and 2016, the proportion of curriculum and pedagogy-related research reached a maximum of 0.785, while the proportion of research in the category of innovative teaching and learning was 0.065. Teaching and learning has always been the dominant research direction, and its importance has always been maintained at more than 0.5 during the 12 years. In contrast, the research on curriculum reached an importance level of about 0.35 in 2013. In-depth study of classroom teaching is an indispensable and critical way to maintain the innovative development and lasting vitality of the discipline of curriculum and pedagogy.

Hierarchical Self-Distilled Feature Learning for Fine-Grained Visual Categorization.

Fine-grained visual categorization (FGVC) relies on hierarchical features extracted by deep convolutional neural networks (CNNs) to recognize closely alike objects. Particularly, shallow layer features containing rich spatial details are vital for specifying subtle differences between objects but are usually inadequately optimized due to gradient vanishing during backpropagation. In this article, hierarchical self-distillation (HSD) is introduced to generate well-optimized CNNs features for accurate fine-grained categorization. HSD inherits from the widely applied deep supervision and implements multiple intermediate losses for reinforced gradients. Besides that, we observe that the hard (one-hot) labels adopted for intermediate supervision hurt the performance of FGVC by enforcing overstrict supervision. As a solution, HSD seeks self-distillation where soft predictions generated by deeper layers of the network are hierarchically exploited to supervise shallow parts. Moreover, self-information entropy loss (SIELoss) is designed in HSD to adaptively soften intermediate predictions and facilitate better convergence. In addition, the gradient detached fusion (GDF) module is incorporated to produce an ensemble result with multiscale features via effective feature fusion. Extensive experiments on four challenging fine-grained datasets show that, with neglectable parameter increase, the proposed HSD framework and the GDF module both bring significant performance gains over different backbones, which also achieves state-of-the-art classification performance.

Supplemental Material for Interleaved Pretesting Enhances Category Learning and Classification Skills

Supplemental Material for Interleaved Pretesting Enhances Category Learning and Classification Skills

Aspect category learning and sentimental analysis using weakly supervised learning

The surge of e-commerce reviews has presented a challenge in manually annotating the vast volume of reviews to comprehend their underlying aspects and sentiments. This research focused on leveraging weakly supervised learning to tackle aspect category learning and the sentiment classification of reviews. Our approach involves the generation of labels for both aspects and sentiments, employing the Snorkel framework of WSL, which incorporates aspect terms, review sentiment scores, and review ratings as sources of weak signals. This innovative strategy significantly reduces the laborious labeling efforts required for processing such extensive datasets. In this study, we deployed hybrid models, namely BiLSTM, CNN-BiLSTM, and CNN-LSTM, which harness multiple inputs, including review text, aspect terms, and ratings. Our proposed model employs two distinct loss functions: Binary Cross Entropy with Sigmoid Activation for Multi-Label Classification, enabling us to learn aspect Labels such as Quality, Usability, Service, Size, and Price, and Categorical Cross Entropy with Softmax Activations for Multi-Class Classification. Subsequently, we meticulously evaluate the performance metrics of these three implemented models, including Macro F1 score and MacroPrecision. CNN & Bi-LSTM model attained 0.78 and 0.79 F1 scores on aspect and sentiment identification, respectively. The outcomes of this research are poised to make a substantial contribution to e-commerce platforms, offering an efficient and automated means to label and analyze vast troves of user reviews.

The Predictive Effect of Cognitive Flexibility and Probability Knowledge on Probability Category Learning

The Predictive Effect of Cognitive Flexibility and Probability Knowledge on Probability Category Learning

The Influence of Self-Directed Learning Assisted by Modules on Mathematical Literacy in Terms of Students' Learning Independence

This research aims to see the effect of self-directed learning assisted by modules on students' mathematical literacy based on students' learning independence. The experimental design used in this research is factorial experimental. The population in this study were all tenth-grade students at MA Plus Walisongo Kotabumi. The samples in this study were students of class X MIA 1 as the experimental and students of class X MIA 2 as the control class for each class of 32 students. The data were subjected to analysis using a two-way ANOVA test. The findings in this research are: 1) there is an influence of self-directed learning assisted by modules on students' mathematical literacy; 2) there is an influence of high, medium, and low learning independence categories on students' mathematical literacy; 3) there is an interaction between self-directed learning assisted by modules and the category of independent learning on students' mathematical literacy.

MANAJEMEN PENGEMBANGAN KEGIATAN BELAJAR MELALUI KECERDASAN KINESTETIK

The title was made in this study about management development learning activities through the kinesthetic intelligence against the background of the problem based on the application curriculum of the 2013 demand the liveliness of the learners in the learning process, because physical motion turned out to have major contributions as revealed based on venon magnesen research say that brain of the human quick to get information from visual modality moving. Learning by doing must be a priority, because student learns 20% by reading, 30% by listening, 40% by looking, 50% by speaking, 60% by doing and 90% by looking, speaking and doing. The goal for obtaining an overview of the steps of planning, implementation, brominated and solutions in developing management development learning activities through the kinesthetic intelligence. Then was able to muster an effective category learning activities as well as efficient or can we take apart that learning it the right grind and grind it correctly. While the research conducted using qualitative methods descriptive observational field as well as supporting documentation. As for the results of the research obtained by the researchers are learning activities through the kinesthetic intelligence methods actually build a very productive learning atmosphere, during the development of the visible learning activities learning well and learners are very enthusiastic for the contribute in learning activities. Then it can be inferred that learning must have a principle of motion, change and evolve, kinesthetic intelligence help the whole body more responsive learning activities, class as a learning space should be one place of learning, and the teacher is able to realize the needs of every learner in the learning. Problems encountered based on explanation of the findings above, it is likely to be passive learners when learning activities are less than creative categories. The recommendations could be given for teachers on request able to conceptualize the active learning activities.

Electronic Portfolio Assessment Instruments in Improving Students' Creative Thinking Skills

The objective of the study is to determine the stage of the process of developing electronic portfolio assessment instruments and obtain a valid and reliable electronic portfolio assessment instrument that can be used as an alternative effective assessment in improving students’ creative thinking on redox reaction materials. The research method employed was Research and Development (RD) with a 4-D development model: define, design, develop, and disseminate. The data sources for this study were obtained from chemistry education lecturers, chemistry teachers, and eleventh-grade students in one of the high schools in Bandung. The instruments used in this study were interview guidelines, instrument content validation sheet, task assessment observation sheet, creative thinking skills assessment task and rubric, portfolio assessment rubric, pre-test and post-test questions and rubrics on chemical reaction equations that have tested for validity. The developed electronic portfolio assessment instrument was realized in a task and assessment rubric based on 21st-century creative thinking indicators. The results reveal that the developed electronic portfolio assessment instrument has good quality because it fulfils the valid requirements with a CVR value of 1.00 and fulfils the reliable requirements with a Cronbach Alpha value of 0.702-0.982. Electronic portfolios can be used as one of the assessment tools for science learning. The developed electronic portfolio assessment instrument may promote students’ creative thinking on redox reaction material based on the N-Gain 0.72 with the high category and has good effectiveness based on the N-Gain 0.61 with the medium category. Based on the category of mastery learning, the effectiveness is 9.66%.

White shark optimizer with optimal deep learning based effective unmanned aerial vehicles communication and scene classification

Unmanned aerial vehicles (UAVs) become a promising enabler for the next generation of wireless networks with the tremendous growth in electronics and communications. The application of UAV communications comprises messages relying on coverage extension for transmission networks after disasters, Internet of Things (IoT) devices, and dispatching distress messages from the device positioned within the coverage hole to the emergency centre. But there are some problems in enhancing UAV clustering and scene classification using deep learning approaches for enhancing performance. This article presents a new White Shark Optimizer with Optimal Deep Learning based Effective Unmanned Aerial Vehicles Communication and Scene Classification (WSOODL-UAVCSC) technique. UAV clustering and scene categorization present many deep learning challenges in disaster management: scene understanding complexity, data variability and abundance, visual data feature extraction, nonlinear and high-dimensional data, adaptability and generalization, real-time decision making, UAV clustering optimization, sparse and incomplete data. the need to handle complex, high-dimensional data, adapt to changing environments, and make quick, correct decisions in critical situations drives deep learning in UAV clustering and scene categorization. The purpose of the WSOODL-UAVCSC technique is to cluster the UAVs for effective communication and scene classification. The WSO algorithm is utilized for the optimization of the UAV clustering process and enables to accomplish effective communication and interaction in the network. With dynamic adjustment of the clustering, the WSO algorithm improves the performance and robustness of the UAV system. For the scene classification process, the WSOODL-UAVCSC technique involves capsule network (CapsNet) feature extraction, marine predators algorithm (MPA) based hyperparameter tuning, and echo state network (ESN) classification. A wide-ranging simulation analysis was conducted to validate the enriched performance of the WSOODL-UAVCSC approach. Extensive result analysis pointed out the enhanced performance of the WSOODL-UAVCSC method over other existing techniques. The WSOODL-UAVCSC method achieved an accuracy of 99.12%, precision of 97.45%, recall of 98.90%, and F1-score of 98.10% when compared to other existing techniques.

Dedication of Machine Learning for Trend of Digital HRM

The digital world has inevitably entered various fields of human life in carrying out their duties as world leaders. Technology is an important tool to ease the human workload, including in this discourse is human resource management. Machine Learning is a technology that allows machines to learn and adapt quickly from given data without having to be explicitly programmed. Machine Learning has found its place in many industries and has great potential to improve the efficiency of human resources within organizations. This research is a literature review of several articles related to machine learning. The review was conducted from some of the recent research efforts that utilize machine learning. Furthermore, this review is derived from multiple literacies and includes an attempt at problem solving efforts that are divided into section areas from the perspective of each machine learning category. Machine learning can change the way the human resource management domain functions in an organization. It is making changes in all aspects of human resource management starting from human resource planning. Enormous data is available in human resource information systems (HRIS) available in organizations.

Profile of Mathematical Literacy of Prospective Teacher Students in Solving Integral Calculus Problems Seen From Learning Independence

This study aims to determine how students’ mathematical literacy is in solving math problems seen from their learning independence. The research method used in this research is descriptive qualitative. The subjects in this study were semester III students at STKIP Kusuma Negara Jakarta, with 6 students, with each category of independent learning consisting of two students. This study used researchers as key instruments, tests to measure students’ mathematical literacy, interview guidelines, and a questionnaire to measure student learning independence. The results of the study show that: informants with high learning independence tend to be able to achieve all indicators of mathematical literacy. Informants with moderate learning independence tend to be able to achieve most indicators of mathematical literacy. Still, they are wrong in using language and symbolic operations, solving mathematical problems and using mathematical tools. Informants with low learning independence, on the other hand, frequently had difficulty completely interpreting the data. As a result, communication of information and problem-solving become flawed.