Different Categories Of Objects Research Articles

An examination of measures of young children's interest in natural object categories

Developmental research utilizes various different methodologies and measures to study the cognitive development of young children; however, the reliability and validity of such measures have been a critical issue in all areas of research practices. To address this problem, particularly in the area of research on infants' interests, we examined the convergent validity of previously reported measures of children's interests in natural object categories, as indexed by (1) parents' estimation of their child's interest in the categories, (2) extrinsic (overt choices in a task), (3) intrinsic (looking time toward objects), and (4) physiological (pupil dilation) responses to objects of different categories. Additionally, we also examined the discriminant validity of all the aforementioned measures against the well-established and validated measure of parents' estimations of children's vocabulary knowledge. Children completed two tasks: (a) an eye-tracking task, where they were presented with images from a range of defined categories, which collected indices of looking time and pupillary activity; (b) a sticker-choice task, where they were asked to choose between two sticker-images from two different categories belonging to the range of categories assessed in the previous task. Parents completed two questionnaires to estimate (i) their child's interests and (ii) vocabulary knowledge in the categories presented. We first analyzed the discriminant validity between the two parent measures, and found a significant positive association between them. Our successive analyses showed no strong or significant associations between any of our measures, apart from a significant positive association between children's looking time and parents' estimations of children's vocabulary knowledge. From our findings, we conclude that measures of infants' interests thus far may not have sufficient reliability to adequately capture any potential relationship between these measures, or index different components of interest in young children. We suggest next steps for further validation studies in infant research.

NIMBY or YIMBY? Understanding the complex relationships between foreign military bases and host societies

Abstract Based on a scoping review of English-language scientific literature for the period of 1990–2022, the article presents a synthetic outline of previous research, focusing on factors influencing the specifics of foreign military bases (FMBs) as examples of facilities that mostly generate negative attitudes known as ‘Not In My Backyard’ (NIMBY). The specific characteristics of different categories of objects that generate various social attitudes are also presented. The results of the analyses include a classification of factors and aspects related to the local impacts of foreign military bases, which have not been analysed before.

Simulation modelling of traffic flow of the east-west latitudinal corridor for socio-economic development of the Novosibirsk region

The article presents the study’s results to assess the degree of impact of different categories of objects on traffic flows in the city of Novosibirsk (Russia). The main feature of the area is its intersection with the Trans-Siberian Railway. The highway is a transport corridor linking states in the European part of the continent with the countries of the Asia-Pacific region. Using structurization, analysis, synthesis, and simulation modeling allowed us to obtain a quantitative and qualitative description of the key objects whose behavior significantly affects the implementation of traffic flows in the study area. Formal rail, road, and pedestrian behavior models are studied and presented. Such models became the basis of simulation modeling, allowing us to estimate the degree of influence of modernization of the Trans-Siberian Railway and the objects of street road infrastructure of Novosibirsk adjacent to the relevant sections of the railway.

FBN: Weakly Supervised Thyroid Nodule Segmentation Optimized by Online Foreground and Background

The main objective of the work described here was to train a semantic segmentation model using classification data for thyroid nodule ultrasound images to reduce the pressure of obtaining pixel-level labeled data sets. Furthermore, we improved the segmentation performance of the model by mining the image information to narrow the gap between weakly supervised semantic segmentation (WSSS) and fully supervised semantic segmentation. Most WSSS methods use a class activation map (CAM) to generate segmentation results. However, the lack of supervision information makes it difficult for a CAM to highlight the object region completely. Therefore, we here propose a novel foreground and background pair (FB-Pair) representation method, which consists of high- and low-response regions highlighted by the original CAM-generated online in the original image. During training, the original CAM is revised using the CAM generated by the FB-Pair. In addition, we design a self-supervised learning pretext task based on FB-Pair, which requires the model to predict whether the pixels in FB-Pair are from the original image during training. After this task, the model will accurately distinguish between different categories of objects. Experiments on the thyroid nodule ultrasound image (TUI) data set revealed that our proposed method outperformed existing methods, with a 5.7% improvement in the mean intersection-over-union (mIoU) performance of segmentation compared with the second-best method and a reduction to 2.9% in the difference between the performance of benign and malignant nodules. Our method trains a well-performing segmentation model on ultrasound images of thyroid nodules using only classification data. In addition, we determined that CAM can take full advantage of the information in the images to highlight the target regions more accurately and thus improve the segmentation performance.

Using representational similarity analysis to reveal category and process specificity in visual object recognition

Cross-condition comparisons on neurodevelopmental conditions are central in neurodiversity research. In the realm of visual perception, the performance of participants with different category-specific disorders such as developmental prosopagnosia (problems with faces) and dyslexia (problems with words) have contributed to understanding of perceptual processes involved in word and face recognition. Alterations in face and word recognition are present in several neurodiverse populations, and improved knowledge about their relationship may increase our understanding of this variability of impairment. The present study investigates organizing principles of visual object processing and their implications for developmental disorders of recognition. Some accounts suggest that distinct mechanisms are responsible for recognizing objects of different categories, while others propose that categories share or even compete for cortical resources. We took an individual differences approach to estimate the relationship between abilities in recognition. Neurotypical participants (N = 97 after outlier exclusion) performed a match-to-sample task with faces, houses, and pseudowords. Either individual features or feature configurations were manipulated. To estimate the separability of visual recognition mechanisms, we used representational similarity analysis (RSA) where correlational matrices for accuracy were compared to predicted data patterns. Recognition abilities separated into face recognition on one hand and house/pseudoword recognition on the other, indicating that face recognition may rely on relatively selective mechanisms in neurotypicals. We also found evidence for a general visual object recognition mechanism, while some combinations of category (faces, houses, words) and processing type (featural, configural) likely rely on additional mechanisms. Developmental conditions may therefore reflect combinations of impaired and intact aspects of specific and general visual object recognition mechanisms, where featural and configural processes for one object category separate from the featural or configural processing of another. More generally, RSA is a promising approach for advancing understanding of neurodiversity, including shared aspects and distinctions between neurodevelopmental conditions of visual recognition.

Опыт практического применения многофункциональной станции для дезинфекции в целях дополнительных мер при проведении истребительных дезинсекционных мероприятий на примере постельных клопов

Today, disinfection institutions, having a small selection of insecticide active ingredients and a huge selection of mix insecticide preparations to combat bed bugs, face the problem of ensuring effective processing of objects of different categories. Keywords: preventive disinfection, fighter disinsection measures, disinsection, chamber disinfection (disinsection), bed bugs, tropical bed bugs, disinsection equipment.

Peculiarities of the Activity of the Brain Structures of People with Schizophrenia During the Categorization of Objects of Animate and Inanimate Nature

One of the features of the work of the brain of people suffering from schizophrenia is changes in the activity of their brain during visual categorization of animate and inanimate objects. The purpose of this study was to analyze the brain activity of people with schizophrenia and their visual categorization of objects with different semantic and physical characteristics. It was assumed that the patterns of brain activity in individuals with schizophrenia would differ from the group of healthy individuals both in the early and late stages of visual processing. Using the method of visual evoked potentials, we studied the features of brain activity in 25 people suffering from schizophrenia from 1 to 7 years old, when they categorized images of animate and inanimate nature, low and high spatial frequency. It was found that the amplitudes of P170 (N170) in the left and right posterior and central leads, as well as the amplitudes of P300 in the central lead in people with schizophrenia do not differ during categorization of animate and inanimate objects, which does not correspond to the data obtained earlier from the people without mental health abnormalities. The revealed result is important for a better understanding of the restructuring of the brain during visual perception of objects of different categories, which occurs during the development of schizophrenia.

Molecules in the peculiar age-defying source IRAS 19312+1950

Context. IRAS 19312+1950 is an isolated infrared source that exhibits a characteristic quasi-point-symmetric morphology in the near-and mid-infrared images and is also very bright in molecular radio lines. Because of its unique observational characteristics, various observational studies have been conducted and several hypotheses have been proposed regarding its origin, which is still unclear. So far, it has been suggested that it could be a peculiar evolved star, a young stellar object, or even a red nova remnant. Regardless of which type of object it is ultimately classified as, IRAS 19312+1950 is exceptionally bright in the infrared and molecular radio lines and therefore will undoubtedly be crucial as a prototype of this kind of object having a peculiar nature or unusual evolutionary phase. Aims. This study aims to reveal the molecular composition of the central part of IRAS 19312+1950 by performing an unbiased molecular radio line survey and discussing the origin of the object from a molecular chemical point of view. Methods. We carried out a spectral line survey with the Institut de Radioastronomie Millimtrique 30 m telescope towards the centre of IRAS 19312+1950 in the 3 and 1.3 mm windows with a frequency coverage of 83.9–91.8 and 218.2–226.0 GHz, respectively. Results. In total, 28 transition lines of 22 molecular species and those isotopologues are detected towards IRAS 19312+1950, some of which exhibit a broad and a narrow component. Seventeen thermal lines and one maser line are newly detected. The molecular species of C17O, 30SiO, HN13C, HC18O+, H2CO, and c-C3H2 are detected for the first time in this object. We calculated the optical depths of the transition lines of 13CO, C18O, HCN, H13CN, and C2H, and determined the rotational temperatures, column densities, and fractional abundances of the detected molecules. We got the isotopic ratios of 12C/13C, 14N/15N, 16O/18O, 16O/17O, 18O/17O, 28Si/30Si, and 29Si/30Si in IRAS 19312+1950 and the values were compared to those of evolved stars, red novae, young stellar objects, and the Sun. The intensities of the molecular radio lines of IRAS 19312+1950 were compared with those of different categories of objects, indicating that the spectral pattern of the broad-line region is similar to that of a red nova or a low-mass young stellar object, while the narrow-line region behaves like an envelope of the asymptotic giant branch star. Conclusions. Our results, in combination with previous studies, favour the hypothesis that IRAS 19312+1950 might be a red nova remnant, in which the progenitors that merged to become a red nova may have contained at least two evolved stars with oxygen-rich and carbon-rich chemistry, respectively.

The origins of the shape bias: Evidence from the Tsimane'.

In the United States, children often generalize the meaning of new words by assuming that objects with the same shape have the same name. We propose that this shape bias is influenced by children's exposure to objects of different categories (artifacts and natural kinds) and language to talk about them. We present a cross-cultural study between English speakers in the United States and Tsimane' speakers in the Bolivian Amazon. We found that U.S. children and adults were more likely to generalize novel labels by shape rather than by material or color, relative to Tsimane' participants. Critically, Tsimane' children and adults systematically avoided generalizing labels to objects that shared no common features with the novel referent. Our results provide initial evidence that the relative exposure to objects of different kinds and language to talk about them can lead to cross-cultural differences on object name learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Augmentative contrastive learning for one-shot object detection

We present an Augmentative Contrastive Learning for One-Shot Object Detection method that is inspired by the co-attention and co-excitation (CoAE) method. In the One-shot Object Detection (OSOD) task, the target image contains rich background information that influences the outcomes of different categories of objects more significantly. As a result, the network frequently suggests objects that do not fall under this category, leading to false detections. To learn the similarity between the target object and the query patch, we built the data augmentation module of object switch by similarity, encourage and restrain module. It primarily consists of the following three components: 1) To improve the contrast between the foreground and background, we propose an object switch module based on data augmentation and feature similarity; 2) To decrease the likelihood of false detections, we propose an encourage and restrain module that allows the network to encourage the recommendation of similar objects while restricting the recommendation of different categories of objects; and 3) To increase classification results, we use the nonlinear projector. We perform tests on the PASCAL VOC and MS COCO datasets to confirm the efficacy of our technique.

A Relation-Augmented Embedded Graph Attention Network for Remote Sensing Object Detection

Multiclass geospatial object detection in high spatial resolution remote sensing imagery (HSRI) is still a challenging task. The main reason is that the objects in HRSI are location-variable and semantic-confusable, which results in the difficulties in differentiating the complicated spatial patterns and deriving the implicitly semantic labels among different categories of objects. In this article, we propose a relation-augmented embedded graph attention network (EGAT), which enables the full exploitation of the underlying spatial and semantic relations among objects for improving the detection performance. Specifically, we first construct two sets of spatial and semantic graphs of objects–objects for object relations modeling. Second, a Siamese architecture-based embedding spatial and semantic graph attention network is designed for relations reasoning, which is implemented by introducing the long short-term memory (LSTM) mechanism into the EGAT, for learning the relations among different categories of intraobjects and interobjects. Driven by the spatial and semantic LSTM, the EGAT-LSTM can adaptively focus on the critical information of reason graphs for spatial–semantic correlation discrimination in the embedding non-Euclidean feature space. By this way, the EGAT-LSTM can effectively capture the global and local spatial–semantic relationships of objects–objects, and then produce relations-augmented features for improving the performance of object detection. We conduct comprehensive experiments on three public datasets for multiclass geospatial object detection. Our method achieves state-of-the-art performance, which demonstrates the superiority and effectiveness of the proposed method.

Realizing balanced object detection through prior location scale information and repulsive loss

Object detection is one significant field of computer vision. The imbalance problem exerts negative effects on achieving satisfactory performance. We reveal two sources of imbalance in existing object detection methods. Correspondingly, we propose our methods in terms of the model architecture and optimization target. Different from general object detection benchmarks, the location distribution of objects with different sizes is unbalanced in many practical applications. In addition, the representation information of different categories of objects is unbalanced. In this paper, we propose a location scale equilibrium module to utilize the prior location scale information and generate more balanced feature maps. More appropriate feature maps are selected and merged for different locations. After merging, feature maps become more consistent in terms of representation content, exerting positive effects on the following classification and regression tasks. For the imbalance caused by similar objects, we propose the repulsive loss to strengthen the punishment. Our method will not treat all categories of objects equally since we take the imbalance between them into consideration. With the enhanced supervision, the training will pay more attention to similar objects. Our proposed model is evaluated on the VisDrone benchmark and UAVDT benchmark. Sufficient experiments are conducted. Our model achieves the highest precision on most evaluation metrics, outperforming the other strong models.

Feature Extraction of Tea Leaf Images using Dual-Tree Complex Wavelet Transform and Gray Level Co-occurrence Matrix

Feature extraction is a very important part of machine learning to analyze and find relationships between objects of different categories. This paper aims to analyze the feature vectors of the tea leaf image produced by using a combination of dual-tree complex wavelet transform and gray level co-occurrence matrix techniques. The tea leaf image consists of four different categories, each representing different phases of tea growth and were acquired using a visible camera from eight different orientations. Feature extraction using Principle Component Analysis (PCA) shows that the texture features can identify a different category of the leaf images without being significantly affected by the difference in scale and orientation of the images.

Software Security Requirement Engineering for Risk and Compliance Management

The objective of the research work is to propose a software based security requirement engineering model using categorical and morphisms theory. The earlier security requirement engineering models focus different viewpoints on parallel processing and develop rewrite based knowledge centred models but does not include different functional mappings between the security objects to select the best strategy. The security models have not considered the needed security functions that are to be implemented in different environments with different levels of executions. The proposed requirement engineering model is based on the formal theory of category of objects and the morphisms between them in addition to n categories and multiple morphisms that were used to organize the security requirement functional objects of different categories. The on demand security requirement objects, morphisms and the uncertain events in any one of the subsystems are considered to manage this security requirement category as an algebraic data types. The collection of security requirement objects using classification and clustering techniques are implicitly applied by the formation of category and morphism. The risk and compliances both in the form of direct and indirect categories are mapped so as to provide a security assurance functors with minimum risk on the requirements to the next design state. An ‘n’ category and ‘n’ morphic model for software security requirement model is proposed towards for minimum security risks through efficient compliance management techniques.

Software Security Requirement Engineering for Risk and Compliance Management

The objective of the research work is to propose a software based security requirement engineering model using categorical and morphisms theory. The earlier security requirement engineering models focus different viewpoints on parallel processing and develop rewrite based knowledge centred models but does not include different functional mappings between the security objects to select the best strategy. The security models have not considered the needed security functions that are to be implemented in different environments with different levels of executions. The proposed requirement engineering model is based on the formal theory of category of objects and the morphisms between them in addition to n categories and multiple morphisms that were used to organize the security requirement functional objects of different categories. The on demand security requirement objects, morphisms and the uncertain events in any one of the subsystems are considered to manage this security requirement category as an algebraic data types. The collection of security requirement objects using classification and clustering techniques are implicitly applied by the formation of category and morphism. The risk and compliances both in the form of direct and indirect categories are mapped so as to provide a security assurance functors with minimum risk on the requirements to the next design state. An ‘n’ category and ‘n’ morphic model for software security requirement model is proposed towards for minimum security risks through efficient compliance management techniques.

Development of Attention and Accuracy in Learning a Categorization Task.

Being able to categorize objects as similar or different is an essential skill. An important aspect of learning to categorize is learning to attend to relevant features (i.e., features that determine category membership) and ignore irrelevant features of the to-be-categorized objects. Feature variability across objects of different categories is informative, because it allows inferring the rules underlying category membership. In this study, participants learned to categorize fictitious creatures (i.e., aliens). We measured attention to the aliens during learning using eye-tracking and calculated the attentional focus as the ratio of attention to relevant versus irrelevant features. As expected, participants’ categorization accuracy improved with practice; however, in contrast to our expectations, their attentional focus did not improve with practice. When computing the attentional focus, attention to the aliens’ eyes was disregarded, because while eyes attract a lot of attention, they did not vary across aliens (non-informative feature). Yet, an explorative analysis of attention to eyes suggested that participants’ attentional focus did become somewhat more efficient in that over time they learned to ignore the eyes. Results are discussed in the context of the need for instructional methods to improve attentional focus in learning to categorize.

A Novel Video Coding Strategy in HEVC for Object Detection

Occupying the most significant portion of global data traffic, video is being generated in almost every aspect of our life. Because of its huge volume, we are depending much more heavily on machine intelligence based analysis. In the meantime, video coding technology has been continuously improved for better compression efficiency. However, the state-of-the-art video coding standards, such as H.265/HEVC and versatile video coding (VVC), are still designed assuming that the compressed video will be watched by a human later. Such a design is not optimal when the compressed video will be used by computer vision applications. While the human visual system (HVS) is consistently sensitive to the content with high contrast, the impact of pixels on computer vision algorithms is task driven. For example, because of the different categories of objects used to train detection algorithms, the influence of the same image content on those detectors also varies. Therefore, human oriented video coding strategies may not be optimal when the compressed signal is further processed by algorithms, as the encoder is unaware of the task specific information. In this article, taking object detection as an example, we propose a novel video coding strategy for computer vision. By protecting the information according to its importance for an object detector rather than for the human visual system, our proposed method has the potential to achieve a better object detection performance with the same bandwidth. The main contributions of our paper are: 1) the modeling of the relationship between object detection accuracy and bit rate; 2) a back propagation based method to analyze the influence of each pixel on the detection of target objects; 3) an object detection oriented bit allocation and codec control parameter determination scheme; 4) an evaluation metric to compare the impact of video coding strategies on a given object detector over a predefined range of bit rate. Experimental results demonstrate that our proposed algorithm can better preserve the video content vital for object detection than state-of-the-art video coding schemes.

Spatially intermixed objects of different categories are parsed automatically

Our visual system is able to separate spatially intermixed objects into different categorical groups (e.g., berries and leaves) using the shape of feature distribution: Determining whether all objects belong to one or several categories depends on whether the distribution has one or several peaks. Despite the apparent ease of rapid categorization, it is a very computationally demanding task, given severely limited “bottlenecks” of attention and working memory capable of processing only a few objects at a time. Here, we tested whether this rapid categorical parsing is automatic or requires attention. We used the visual mismatch negativity (vMMN) ERP component known as a marker of automatic sensory discrimination. 20 volunteers (16 female, mean age—22.7) participated in our study. Loading participants’ attention with a central task, we observed a substantial vMMN response to unattended background changes of categories defined by certain length-orientation conjunctions. Importantly, this occurred in conditions where the distributions of these features had several peaks and, hence, supported categorical separation. These results suggest that spatially intermixed objects are parsed into distinct categories automatically and give new insight into how the visual system can bypass the severe processing restrictions and form rich perceptual experience.

Exploring the Role of Action Consequences in the Handle-Response Compatibility Effect.

Previous research investigating handle-response compatibility effects with graspable objects used different categories of objects as stimuli, regardless of their specific, intrinsic characteristics. The current study explores whether different types of objects’ characteristics may elicit different types of spatial compatibility, that is, handle-response and response-effect compatibility as well as their potential interaction. In Experiment 1, objects having a graspable handle opposite to either a visible functional component (i.e., handle-function objects: a teapot) or a latent functional component (handle-only objects: a pitcher lacking the spout) were presented separately in different blocks. Both the handle and the goal-directed functional components of these objects were located on the horizontal axis. In Experiment 2, handle-only objects had a handle located on the horizontal axis and a latent functional component located on the vertical axis (e.g., a cup). In both experiments, participants were required to judge the material (plastic and metal) the object was made of. Results showed that the handle-response compatibility effect was sensitive to whether the actions consequences of object manipulation took place on the horizontal rather than on the vertical axis.

Office Garbage Intelligent Classification Based on Inception-v3 Transfer Learning Model

With the increase in garbage production, the problem of garbage pollution is becoming more and more serious. Garbage recognition and classification can reduce the environmental burden, but there are still some challenges. Image classification, an image processing method that separates different categories of objects according to different characteristics reflected in the image information. This paper collected and produced an image data set with 2313 photos of different office garbage, and proposed an intelligent classification garbage can to solve the realistic problems. That method based on transfer techniques to retain the excellent feature extraction ability of the Inception-v3 model of TensorFlow, which can recognize objects through the convolutional neural network model. The experimental results showed that the garbage classification effect was obvious and the average accuracy rate reached 95.33%.