Improvements of Statistical Learning Skills Allow Older Children to Go Beyond Single-Hypothesis Testing When Learning Words

Children learn words in ambiguous situations, where multiple objects can potentially be referents for a new word. Yet, researchers debate whether children maintain a single word-object hypothesis – and revise it if falsified by later information – or whether children establish a network of word-object associations whose relative strengths are modulated with experience. To address this issue, we presented 4- to 12-year-old children with sets of mutual exclusivity (fast-mapping) trials: offering them with obvious initial hypotheses (that the novel object is the referent for the novel word). We observe that children aged six years and above, despite showing a novelty bias and retaining this novel word – novel object association, also formed an association between the novel word and the name-known object, thereby suggesting that older children attend to more than one word-object association, in a manner similar to associative learning. We discuss our findings in the context of competing theoretical accounts related to word learning.

Face the music: Children’s facial affect in musical imitation and improvisation tasks

The purpose of this study was to examine facial affect of young children who completed a singing task that included imitation and improvisation. Eighty-nine children (4–6 years: 45 male and 44 female participants) completed three singing conditions from a standard singing test battery (i.e., Advancing Interdisciplinary Research in Singing-Test Battery of Singing Skills [AIRS-TBSS]). These included singing a favorite song, imitating a song, and improvising a song ending. Facial affect was analyzed with Noldus FaceReader software, and subjective responses also were collected. Results revealed children exhibited a happy emotion most prominently during the improvisation and favorite song conditions compared with the imitation condition. However, a higher percentage of surprised emotions were found during the imitation condition. Frequency analysis revealed a significantly different range and final note for the improvisation condition compared with imitation. Children’s self-reported ratings of happiness were related to their displayed facial affect (i.e., happiness) scores in FaceReader ( p < .05). Qualitative data analysis revealed three emerging themes of song familiarity, object association, and song preference. Children exhibited more positive affect when singing a favorite song or improvising. Based on the type of vocal performance task, it is necessary to consider how young children respond to vocal tasks.

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies.

A real-time semantic visual SLAM approach with points and objects

Visual simultaneously localization and mapping (SLAM) is important for self-localization and environment perception of service robots, where semantic SLAM can provide a more accurate localization result and a map with abundant semantic information. In this article, we propose a real-time PO-SLAM approach with the combination of both point and object measurements. With point–point association in ORB-SLAM2, we also consider point–object association based on object segmentation and object–object association, where the object segmentation is employed by combining object detection with depth histogram. Also, besides the constraint of feature points belonging to an object, a semantic constraint of relative position invariance among objects is introduced. Accordingly, two semantic loss functions with point and object information are designed and added to the bundle adjustment optimization. The effectiveness of the proposed approach is verified by experiments.