Object Labels Research Articles

Do children treat adjectives and nouns differently as modifiers in prenominal position?

Usage-based theories of children's syntactic acquisition (e.g., Tomasello, 2000a) predict that children's abstract lexical categories emerge from their experience with particular words in constructions in their input. Because modifiers in English are almost always prenominal, children might initially treat adjectives similarly to nouns when used in a prenominal position. In this study, we taught English-speaking preschoolers (between 2 and 6 years) novel nouns (object labels) and adjectives (words referring to attributes) in both prenominal and postnominal positions. The children corrected both postnominal adjectives and nouns to prenominal position, but corrected modifying nouns more often than adjectives. These results suggest that children differentiate between nouns and adjectives even when they occur in the same position and serve the same function (i.e., modification). Children were increasingly likely to correct postnominal adjectives (not nouns) with increasing age. We argue that children attend to word order more when it makes a difference in meaning.

Late Talkers can generalise trained labels by object shape similarities, but not unfamiliar labels.

Late talkers (LTs) exhibit delayed vocabulary development, which might stem from a lack of a typical word learning strategy to generalise object labels by shape, called the 'shape bias'. We investigated whether LTs can acquire a shape bias and whether this accelerates vocabulary learning. Fourteen LTs were randomly allocated to either a shape training group (Mage = 2.76 years, 6 males), which was taught that objects similar in shape have the same name, or a control group (Mage = 2.61 years, 4 males), which was taught real words without any focus on object shape. After seven training sessions, children in the shape training group generalised trained labels by shape (d = 1.28), but not unfamiliar labels. Children in the control group extended all labels randomly. Training did not affect expressive vocabulary.

Dogs with a vocabulary of object labels retain them for at least 2 years.

Long-term memory of words has a crucial role in the developing abilities of young children to acquire language. In dogs, the ability to learn object labels is present in only a small group of uniquely gifted word learner (GWL) dogs. As they are very rare, little is known about the mechanisms through which they acquire such large vocabularies. In the current study, we tested the ability of five GWL dogs to retrieve 12 labelled objects 2 years after the object-label mapping acquisition. The dogs proved to remember the labels of between three and nine objects. The results shed light on the process by which GWL dogs acquire an exceptionally large vocabulary of object names. As memory plays a crucial role in language development, these dogs provide a unique opportunity to study label retention in a non-linguistic species.

Neighborhood margin rough set: Self-tuning neighborhood threshold

The neighborhood threshold in the neighborhood rough set has a significant impact on the neighborhood relation. When the neighborhood threshold of an object exceeds the critical value, the labels of objects in the neighborhood are not completely consistent, and the critical value of each object often differs. Most existing neighborhood rough set models cannot adaptively regulate the neighborhood threshold. In this paper, we introduce a novel neighborhood rough set model that incorporates a self-tuning mechanism for the neighborhood threshold, taking into account the distribution of objects across different areas. The neighborhood margin is a measure proposed to assess the condition of neighborhoods, and it is calculated by subtracting the neighborhood threshold from the closest distance between heterogeneous elements. The neighborhood margin accurately represents the local state of the neighborhood, taking into account decision information. The margin neighborhood is proposed with a self-tuning the neighborhood threshold. Finally, we introduce the margin neighborhood rough set model and margin neighborhood-based attribute reduction algorithm, and explore the relationship between the proposed model and the neighborhood rough set model. The experiment examines the performance of reducts under various measures, and demonstrates that the neighborhood margin rough set reduces the uncertainty of neighborhood granules effectively, leading to excellent classification performance compared to other neighborhood-based SOTA models.

Evaluating the psychometric properties of ChatGPT-generated questions

Not much is known about how LLM-generated questions compare to gold-standard, traditional formative assessments concerning their difficulty and discrimination parameters, which are valued properties in the psychometric measurement field. We follow a rigorous measurement methodology to compare a set of ChatGPT-generated questions, produced from one lesson summary in a textbook, to existing questions from a published Creative Commons textbook. To do this, we collected and analyzed responses from 207 test respondents who answered questions from both item pools and used a linking methodology to compare IRT properties between the two pools. We find that neither the difficulty nor discrimination parameters of the 15 items in each pool differ statistically significantly, with some evidence that the ChatGPT items were marginally better at differentiating different respondent abilities. The response time also does not differ significantly between the two sources of items. The ChatGPT-generated items showed evidence of unidimensionality and did not affect the unidimensionality of the original set of items when tested together. Finally, through a fine-grained learning objective labeling analysis, we found greater similarity in the learning objective distribution of ChatGPT-generated items and the items from the target OpenStax lesson (0.9666) than between ChatGPT-generated items and adjacent OpenStax lessons (0.6859 for the previous lesson and 0.6153 for the subsequent lesson). These results corroborate our conclusion that generative AI can produce algebra items of similar quality to existing textbook questions that share the same construct or constructs as those questions.

A novel hybrid methodology integrating pixel- and object-based techniques for mapping land use and land cover from high-resolution satellite data

ABSTRACT The classification of very high-resolution satellite imagery remains a focal point in remote sensing, attracting increased attention across diverse scientific disciplines. Various classification methods, including pixel- and object-based techniques, have been proposed, and their performances and limitations have been discussed in the literature. This paper presents a hybrid method that combines the strengths of pixel- and object-based methods in image classification to minimize errors associated with the segmentation process, particularly under-segmentation errors in object-based image analysis. The core concept behind the method lies in categorizing segmented image objects as either homogeneous or heterogeneous based on their class probability. In this process, the estimated possibilities from the object-based classification model are considered, and segments are designated as homogeneous or heterogeneous using a user-defined threshold. The object-based classification model determines the class labels for homogeneous image objects, while the heterogeneous ones, containing pixels representing different land cover classes, are classified using the pixel-based model. The performance of hybrid classification models, created by varying thresholds, is analysed using high-resolution WorldView-3 and WorldView-2 imagery and compared with pixel- and object-based classification results. For the implementation of image classification methods, Canonical Correlation Forest (CCF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) were employed. The findings indicated that employing the suggested hybrid strategy with a threshold value selected within a specific range (e.g. between 60% and 80%) and employing a robust classification algorithm that provides class probabilities (e.g. CCF) results in a statistically significant improvement in overall accuracy compared to pixel and object-based methods, with gains of 5% and 4%, respectively. Visual analysis of the produced thematic maps revealed that the proposed method minimizes the salt-and-pepper effect associated with pixel-based classification while mitigating segmentation problems arising from object-based classification.

Влияние пола и когнитивного стиля на особенности движений глаз при навигации по карте

<p>We investigated the effects of sex and cognitive style on performance in the map navigation task with various label types (object labels, verbal labels, no labels) in 40 healthy participants (20 men, 20 women, age 24,3±0,8) using eye tracking. The cognitive styles (object, spatial, verbal) were assessed using a questionnaire developed by Blazhenkova and Kozhevnikov [2009]. Only men had a reduced number of fixations during route reproduction compared to route memorization; women had longer saccade durations than men. Participants with spatial cognitive style showed shorter saccade duration compared to object cognitive style. Participants with spatial and verbal cognitive styles had more fixations on maps with verbal labels than on the other maps. Analysis of questionnaire and the number of fixations has demonstrated the link between sex and cognitive style, but the effects of sex and cognitive style are not identical. Sex affects eye movements during the performance of different tasks (map familiarization, route memorization, and route reproduction), while cognitive style determines eye movements during navigation on maps with different types of labels.</p>

Linguistic Landscape Analysis of Identity Representation in Public Signs at Watugong Buddhist Monastery Semarang

Public signs at Watugong Monastery in Semarang serve not only as guides for visitors but also as representations of identity, meaning, and values. This study aims to uncover the meaning and representation of identity in the public signs of Watugong Monastery by considering the cultural, religious, and social contexts in accordance with the dominant ideology in the place. This study uses a qualitative descriptive method with a Barthes (1991) semiotics approach. Research data was collected through observation and documentation methods using photography techniques. The results of this study show that there are representations of cultural, religious, and socio-religious identities in the Public signs of Watugong Monastery. Religious identity comes from the interpretation of the object label signs 福 (fú), 祿 (lù), and 壽 (shòu) which reflect Chinese cultural values. Religious identity emerges from hiolo stick sign, which contains the meaning of strong faith in Buddha and his teachings through prayer and devotion supported by morality and exemplary behavior. Finally, socio-religious identity appears on the Ashoka inscription sign, the strong religious element derived from Buddhist teachings, the inscription also shows an example of social life between people of different faiths who must always teach tolerance and moderation in religion. This research is still very open for further research in various other aspects of LL, for example, visitors' perceptions of the language used at Watugong Monastery.

Comparing the productive vocabularies of grey parrots (Psittacus erithacus) and young children

Due to their outstanding ability of vocal imitation, parrots are often kept as pets. Research has shown that they do not just repeat human words. They can use words purposefully to label objects, persons, and animals, and they can even use conversational phrases in appropriate contexts. So far, the structure of pet parrots’ vocabularies and the difference between them and human vocabulary acquisition has been studied only in one individual. This study quantitatively analyses parrot and child vocabularies in a larger sample using a vocabulary coding method suitable for assessing the vocabulary structure in both species. We have explored the composition of word-like sounds produced by 21 grey parrots (Psittacus erithacus) kept as pets in Czech- or Slovak-speaking homes, and compared it to the composition of early productive vocabularies of 21 children acquiring Czech (aged 8–18 months), who were matched to the parrots by vocabulary size. The results show that the ‘vocabularies’ of talking grey parrots and children differ: children use significantly more object labels, activity and situation labels, and emotional expressions, while parrots produce significantly more conversational expressions, greetings, and multiword utterances in general. These differences could reflect a strong link between learning spoken words and understanding the underlying concepts, an ability seemingly unique to human children (and absent in parrots), but also different communicative goals of the two species.

PatchSorter: a high throughput deep learning digital pathology tool for object labeling

The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities of histological objects. Here we release an open-source labeling tool, PatchSorter, which integrates deep learning with an intuitive web interface. Using >100,000 objects, we demonstrate a >7x improvement in labels per second over unaided labeling, with minimal impact on labeling accuracy, thus enabling high-throughput labeling of large datasets.

Trusting information from friends: Adults expect it but preschoolers do not.

As adults, we might understand that beliefs often spread because people are strongly influenced by their friends, family, and other social connections. However, do we think those influences are strong enough to overrule direct evidence of a friend's unreliability? And do preschoolers expect people to show such biases toward friends and to privilege friendship over reliability? Across three experiments, we explored whether friendship influences the evaluations of trust when others learn labels for novel objects as well as personal opinions. After watching scenes involving a main character, her best friend, and a stranger, preschoolers and adults judged who would be trusted for information from the main character's perspective (third person) as well as their own (first person). Adults (n = 128, 55 female, recruited online from across the United States) expected the main character to trust information from her friend even if she had been previously inaccurate, while basing their own first-person judgments on accuracy. In contrast, 4- and 5-year-olds (n = 128, 62 female, from the United States) thought that the main character would be like themselves and prioritize accuracy over friendship. Further, preschoolers expected the main character to trust her (inaccurate) friend and (accurate) stranger equally when forming personal opinions. Thus, young children, unlike adults, do not expect others' epistemic trust to privilege friendship with the speaker over accuracy information. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Toddlers' prior social experience with speakers influences their word learning.

Toddlers prefer to learn from familiar adults, particularly their caregivers, and perform better on word learning tasks when taught by caregivers than by strangers. However, it remains unclear why toddlers learn better from caregivers than from strangers. One possibility is that toddlers are more receptive to learning from individuals whom they have found to be engaging in previous interactions. The current study tested whether toddlers learn more from an unfamiliar adult who was previously engaging than from an unfamiliar adult who was previously unengaging. Toddlers (27-29months, N=40) were taught labels for novel objects by two different experimenters. Prior to word learning, toddlers watched pre-recorded videos of one experimenter utilizing engaging behaviors (i.e., using infant-directed speech, gestures, eye contact, and positive affect) and one experimenter utilizing unengaging behaviors (i.e., using adult-directed speech, no gestures, no eye contact, and neutral affect). Both experimenters were equally engaging during labeling. Word learning was then tested using a looking-while-listening paradigm. The results of linear mixed-effects model, cluster-based permutation, and growth curve analyses suggest heightened performance for words that were taught by the experimenter who was previously engaging. These results begin to reveal the kinds of social experiences that promote success in early word learning.

Unraveling the relationship between coastal landscapes and sentiments: An integrated approach based on social media data and interpretable machine learning methods

AbstractCoastal landscapes exert a significant impact on the human sentimental perceptions and physical and mental well‐being of people. However, little is known about explicitly linking between the landscape characteristics and people's sentimental preferences expressed in social media data. The main objective of this study was to explore the nonlinear and interaction effects of key factors that influenced sentiments in the coastal areas of Hong Kong, considering both subjective landscape preferences and objective landscape patterns. We quantified users' sentiment polarity based on the crowdsourcing textual data of Flickr. To study users' subjective landscape preferences, we computed various visual landscape objects' proportion in images. Meanwhile, eight user clusters and nine image clusters were detected by the identified visual object labels. We quantified objective landscape patterns considering the land use pattens and the availability of public service facilities. Finally, we utilized an interpretable classification model to analyze the factors that may affect sentiments and their interplay interactions. We found that ecotourism‐related clusters exhibited the most positive sentiment. The proportion of floor and sky pixels in images exhibits the highest global relative importance when predicting sentiments. This study extends a new insight on the relationship between landscape characteristics and sentiments from both subjective and objective perspectives based on social media data and interpretable machine learning methods. This research may help decision‐makers in designing landscapes that aptly satisfy to the needs of the public and promote sustainable management of the coastal environment.

Effects of dual- and single-language exposure on children’s word learning: Experimentally testing the role of competition

This study investigated the role of dual- and single-language input in bilingual children’s word learning. In Experiment 1, 26 Spanish–English bilingual children aged 4 and 5 years (9 girls; 73% Latino; 65% White) learned novel words in single- and dual-language conditions. In the single-language condition, children learned English-like labels for novel objects. In the dual-language condition, the same children learned Spanish- and English-like labels for a different set of objects; all labels were produced by the same bilingual speaker, creating competition between the two languages. A second group of bilingual children (N = 25; 14 girls; 72% Latino; 40% White) participated in Experiment 2, which tested whether tagging language by speaker in the dual-language condition (mimicking the one person–one language input strategy) would influence performance. In both experiments, participants learned novel English words above chance (ps < .05) in both conditions, with better performance in the single-language condition. These results indicate an advantage for single-language learning contexts, but the theoretical roots and the practical value of this advantage are unclear.

Imitation of Computer-Generated Sounds by Wild Atlantic Spotted Dolphins (Stenella frontalis)

Since 1985 a community of wild Atlantic spotted dolphin (Stenella frontalis) have been observed underwater in the Bahamas. A human-worn, acoustic underwater two-way communication interface was developed and deployed from 2013-2016. Dolphins were exposed to an acoustic referentially based wearable underwater computer/interface. A model/rival system was used with dolphins and human participants during in-water sessions. Artificial and natural objects were labeled with computer generated sounds. Female juvenile spotted dolphins dominated the activity. Group size averaged seven dolphins for an average duration of 37 minute over 58 sessions. Of 243 video audio imitations and 56 Cetacean Hearing Augmentation Telemetry (CHAT) audio imitations, six potential response types were documented and measured. Stand-alone vocal contour mimics and Frequency Modulated Contours were the most common imitations. Within 5 sec of a computer-generated sound playing, of the 191 non-stand-alone vocal responses that were produced, 114 of them (59.7%) were judged as partial accurate matches, 3 of them (1.57%) were judged as non-matching partial imitations of a computer-generated sound, 67 of them (35.08%) were signature whistles, and seven of them (3.67%) were either non-signature whistle vocalizations or a mimic of the start or end tones. Thus, the majority of vocalizations produced by the dolphins within five seconds of a computer-generated sound were partial accurate imitations for the computer-generated sound played. Dolphins demonstrated both immediate and delayed vocal imitation and flexible attempts at imitation but did not show signs of a functional understanding of object labels. Atlantic spotted dolphins showed vocal flexibility in reaction to humans broadcasting computer generated sounds.

Quantifying task-related gaze.

Competing theories attempt to explain what guides eye movements when exploring natural scenes: bottom-up image salience and top-down semantic salience. In one study, we apply language-based analyses to quantify the well-known observation that task influences gaze in natural scenes. Subjects viewed ten scenes as if they were performing one of two tasks. We found that the semantic similarity between the task and the labels of objects in the scenes captured the task-dependence of gaze (t(39) = 13.083; p < 0.001). In another study, we examined whether image salience or semantic salience better predicts gaze during a search task, and if viewing strategies are affected by searching for targets of high or low semantic relevance to the scene. Subjects searched 100 scenes for a high- or low-relevance object. We found that image salience becomes a worse predictor of gaze across successive fixations, while semantic salience remains a consistent predictor (X2(1, N=40) = 75.148, p < .001). Furthermore, we found that semantic salience decreased as object relevance decreased (t(39) = 2.304; p = .027). These results suggest that semantic salience is a useful predictor of gaze during task-related scene viewing, and that even in target-absent trials, gaze is modulated by the relevance of a search target to the scene in which it might be located.

When is it right for a robot to be wrong? Children trust a robot over a human in a selective trust task

Little is known about how children perceive, trust and learn from social robots compared to humans. The goal of this study was to compare a robot and a human agent in a selective trust task across different combinations of reliability (both reliable, only human reliable, or only robot reliable). 111 children, aged 3 to 6 years, participated in an online study where they viewed videos of a human and a robot labelling both familiar and novel objects. We found that, although children preferred to endorse a novel object label from the agent who previously labelled familiar objects correctly, when both the human and the robot were reliable they were biased more towards the robot. Their social evaluations also tended much more strongly towards a general robot preference. Children’s conceptualisations of the agents making a mistake also differed, such that an unreliable human was selected as doing things on purpose, but not an unreliable robot. These findings suggest that children’s perceptions of a robot’s reliability are separate from their evaluation of its desirability as a social interaction partner and its perceived agency. Further, they indicate that a robot making a mistake does not necessarily reduce children’s desire to interact with it as a social agent.

Development of a Wafer Defect Pattern Classifier Using Polar Coordinate System Transformed Inputs and Convolutional Neural Networks

Defect pattern analysis of wafer bin maps (WBMs) is an important means of identifying process problems. Recently, automated analysis methods using machine learning or deep learning have been studied as alternatives to manual classification by engineers. In this paper, we propose a method to improve the feature extraction performance of defect patterns by transforming the polar coordinate system instead of the existing WBM image input. To reduce the variability of the location representation, defect patterns in the Cartesian coordinate system, where the location of the distributed defect die is not constant, were converted to a polar coordinate system. The CNN classifier, which uses polar coordinate transformed input, achieved a classification accuracy of 91.3%, which is 4.8% better than the existing WBM image-based CNN classifier. Additionally, a tree-structured classifier model that sequentially connects binary classifiers achieved a classification accuracy of 94%. The method proposed in this paper is also applicable to the defect pattern classification of WBMs consisting of different die sizes than the training data. Finally, the paper proposes an automated pattern classification method that uses individual classifiers to learn defect types and then applies ensemble techniques for multiple defect pattern classification. This method is expected to reduce labor, time, and cost and enable objective labeling instead of relying on subjective judgments of engineers.

The Icing on the Cake. Or Is it Frosting? The Influence of Group Membership on Children's Lexical Choices.

Adults are skilled at using language to construct/negotiate identity and to signal affiliation with others, but little is known about how these abilities develop in children. Clearly, children mirror statistical patterns in their local environment (e.g., Canadian children using zed instead of zee), but do they flexibly adapt their linguistic choices on the fly in response to the choices of different peers? To address this question, we examined the effect of group membership on 7- to 9-year-olds' labeling of objects in a trivia game, exploring whether they were more likely to use a particular label (e.g., sofa vs. couch) if members of their "team" also used that label. In a preregistered study, children (N = 72) were assigned to a team (red or green) and were asked during experimental trials to answer questions-which had multiple possible answers (e.g., blackboard or chalkboard)-after hearing two teammates and two opponents respond to the same question. Results showed that children were significantly more likely to produce labels less commonly used by the community (i.e., dispreferred labels) when their teammates had produced those labels. Crucially, this effect was tied to group membership, and could not be explained by children simply repeating the most recently used label. These findings demonstrate how social processes (i.e., group membership) can guide linguistic variation in children.

Multitargets Joint Training Lightweight Model for Object Detection of Substation.

The object detection of the substation is the key to ensuring the safety and reliable operation of the substation. The traditional image detection algorithms use the corresponding texture features of single-class objects and would not handle other different class objects easily. The object detection algorithm based on deep networks has generalization, and its sizeable complex backbone limits the application in the substation monitoring terminals with weak computing power. This article proposes a multitargets joint training lightweight model. The proposed model uses the feature maps of the complex model and the labels of objects in images as training multitargets. The feature maps have deeper feature information, and the feature maps of complex networks have higher information entropy than lightweight networks have. This article proposes the heat pixels method to improve the adequate object information because of the imbalance of the proportion between the foreground and the background. The heat pixels method is designed as a kind of reverse network calculation and reflects the object's position to the pixels of the feature maps. The temperature of the pixels indicates the probability of the existence of the objects in the locations. Three different lightweight networks use the complex model feature maps and the traditional tags as the training multitargets. The public dataset VOC and the substation equipment dataset are adopted in the experiments. The experimental results demonstrate that the proposed model can effectively improve object detection accuracy and reduce the time-consuming and calculation amount.