Images Of Animals Research Articles

Meowmy, Pawrents and Menschenwelpen ‘human puppies’: Linguistic practices of Doing Interspecies Families on German Instagram

Looking at cute animal images and videos has become a popular online pastime and animals themselves increasingly appear as actors in social media who have their own accounts and profiles from which they ‘speak’ to and ‘interact’ with us. This development goes hand in hand with a change in human-animal relations in most of Western cultures, where pets – especially cats and dogs – are more and more often seen and treated as family members while the animals we eat are rendered invisible in factory farms that are spatially detached from everyday human life. The familial relationships between humans and their pet companions are constructed by a variety of sociocultural means, one of which is language. By talking about pets as babies and children and oneself as cat or dog moms, the connection between the human and the pet is discursively established as one of kinship and intimacy rather than one of ownership and obedience. Social media have become a prolific space for performing “interspecies families” (Owens, 2015), where self-acclaimed pet parents can interact with each other and build communities of practice in which pet accounts post and engage with each other from an imagined pet perspective. This article uses a quantitative corpus-based approach to study the discursive construction of interspecies families on German-speaking Instagram. By analysing more than 20,000 posts from dog and cat accounts offering first person narratives, the article provides insights into the ways human pet owners speak about themselves and their relations with their pets from an imagined pet perspective. The analysis shows that the caption texts accompanying the posted pictures and videos are highly anthropocentric: at the centre of these short texts stands the – usually female – human pet owner. Using family terminology for both humans and other animals in these posts serves the linguistic construction of interspecies families in which pets and their human companions are held together by love and kinship ties rather than ownership.

Analyze the Impact, Reasons, and Optimization Strategies of the Image of AI Anchors on Consumer Purchase Intention in Live Streaming E-commerce

The importance of live streaming e-commerce in online shopping is increasing with the continuous development of live streaming technology. Artificial intelligence (AI), as a disruptive force, has introduced virtual anchors in the live streaming field, replacing live anchors for product introduction and interaction. The virtual anchor image, with its anthropomorphic appearance, interacts with the audience in real-time and is gradually being applied in various fields such as e-commerce, advertising, and social platforms. This study reviews how the image of AI anchors in live streaming e-commerce affects consumer trust and personalized experience, and further explores its impact on consumer purchase intention. Research has found that virtual anchor images can be divided into human and animal images based on their appearance, and different images have different impacts on consumer purchasing intentions. Humanized virtual anchors establish trust relationships and stimulate purchase intentions through rich expressions and actions; Personalized and customized design enhances the attractiveness and sense of identity of virtual anchors. Finally, regarding the application of enterprises in the field of virtual anchors, this article proposes anthropomorphic design, personalized image, and cultural adaptability strategies to improve the commercial efficiency and brand image of virtual anchors.

Aerial Wildlife Image Repository for animal monitoring with drones in the age of artificial intelligence.

Drones (unoccupied aircraft systems) have become effective tools for wildlife monitoring and conservation. Automated animal detection and classification using artificial intelligence (AI) can substantially reduce logistical and financial costs and improve drone surveys. However, the lack of annotated animal imagery for training AI is a critical bottleneck in achieving accurate performance of AI algorithms compared to other fields. To bridge this gap for drone imagery and help advance and standardize automated animal classification, we have created the Aerial Wildlife Image Repository (AWIR), which is a dynamic, interactive database with annotated images captured from drone platforms using visible and thermal cameras. The AWIR provides the first open-access repository for users to upload, annotate, and curate images of animals acquired from drones. The AWIR also provides annotated imagery and benchmark datasets that users can download to train AI algorithms to automatically detect and classify animals, and compare algorithm performance. The AWIR contains 6587 animal objects in 1325 visible and thermal drone images of predominantly large birds and mammals of 13 species in open areas of North America. As contributors increase the taxonomic and geographic diversity of available images, the AWIR will open future avenues for AI research to improve animal surveys using drones for conservation applications. Database URL: https://projectportal.gri.msstate.edu/awir/.

A content analysis of cannabis edibles package marketing in the United States

Background: With states legalizing cannabis at a rapid pace, and the increasing popularity of edibles, it is important to document marketing practices to better understand how they might be appealing and misleading to consumers to guide state policymakers. Methods: A descriptive content analysis of 1229 cannabis edible packages advertised on a publicly available website between June and November 2022 and available for sale in licensed dispensaries was performed. Results: Healthy ingredient descriptors were the most common type of descriptor with 31 % of packages including words like “vegan”, “gluten free” and “natural”. Quality descriptors like “handcrafted” were on 28 % of packages. Other descriptors were focused on the consumer experience including expected effects (e.g., “relax”) (27 %), taste or flavor (e.g., “sour”) (21 %) and pharmacokinetics (e.g., “fast-acting”) (19 %). Images of non-cannabis plants and outdoor nature settings were on half of packages. Images of the cannabis plant were on 33 % of packages. Flavor imagery including images of food were common (43 %). Other marketing appeals included images of people (15 %), animals (12 %) and space (10 %). Conclusions: Package marketing used by other commercial industries was common on cannabis edible packages. Edibles marketing is distinct from other cannabis products in its ability to focus on the food ingredients which could mislead consumers into thinking the cannabis, rather than the food, is healthy or less harmful. Research examining the impact of cannabis edibles marketing strategies on appeal and harm perceptions is critically needed to guide policymakers as they establish packaging regulations to optimize public health and safety.

Interaction between the prefrontal and visual cortices supports subjective fear.

It has been reported that threatening and non-threatening visual stimuli can be distinguished based on the multi-voxel patterns of haemodynamic activity in the human ventral visual stream. Do these findings mean that there may be evolutionarily hardwired mechanisms within early perception, for the fast and automatic detection of threat, and maybe even for the generation of the subjective experience of fear? In this human neuroimaging study, we presented participants ('fear' group: N = 30; 'no fear' group: N = 30) with 2700 images of animals that could trigger subjective fear or not as a function of theindividual's idiosyncratic 'fear profiles' (i.e. fear ratings of animals reported by a given participant). We provide evidence that the ventral visual stream may represent affectively neutral visual features that are statistically associated with fear ratings of participants, without representing the subjective experience of fear itself. More specifically, we show that patterns of haemodynamic activity predictive of a specific 'fear profile' can be observed in the ventral visual stream whether a participant reports being afraid of the stimuli or not. Further, we found that the multivariate information synchronization between ventral visual areas and prefrontal regions distinguished participants who reported being subjectively afraid of the stimuli from those who did not. Together, these findings support the view that the subjective experience of fear may depend on the relevant visual information triggering implicit metacognitive mechanisms in the prefrontal cortex. This article is part of the theme issue 'Sensing and feeling: an integrative approach to sensory processing and emotional experience'.

Bi-Directional Full-Color Generation and Tri-Channel Information Encoding Based on a Plasmonic Metasurface.

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included, has rarely been investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by the rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device is numerically confirmed to generate full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, the information encoding process is demonstrated for three multiplexed animal images and quick-responsive (QR) codes to verify the efficient information encoding and decoding of the proposed scheme. In the simulation, the animals can be seen under different polarization incidences, and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding.

Neural Representations of Sensory Uncertainty and Confidence Are Associated with Perceptual Curiosity.

Humans are immensely curious and motivated to reduce uncertainty, but little is known about the neural mechanisms that generate curiosity. Curiosity is inversely associated with confidence, suggesting that it is triggered by states of low confidence (subjective uncertainty), but the neural mechanisms of this link, have been little investigated. Inspired by studies of sensory uncertainty, we hypothesized that visual areas provide multivariate representations of uncertainty, which are read out by higher-order structures to generate signals of confidence and, ultimately, curiosity. We scanned participants (17 female, 15 male) using fMRI while they performed a new task in which they rated their confidence in identifying distorted images of animals and objects and their curiosity to see the clear image. We measured the activity evoked by each image in the occipitotemporal cortex (OTC) and devised a new metric of "OTC Certainty" indicating the strength of evidence this activity conveys about the animal versus object categories. We show that, perceptual curiosity peaked at low confidence and OTC Certainty negatively correlated with curiosity, establishing a link between curiosity and a multivariate representation of sensory uncertainty. Moreover, univariate (average) activity in two frontal areas-vmPFC and ACC-correlated positively with confidence and negatively with curiosity, and the vmPFC mediated the relationship between OTC Certainty and curiosity. The results reveal novel mechanisms through which uncertainty about an event generates curiosity about that event.

The Amplification of Intuitive Archetypal Images with Clients: The Fox Image in Modern China

ABSTRACT This paper will provide a case study to show how the psychoanalyst can use the intuitive archetypal image from a client in the analytic process. The animal image occurred in the analysis has been amplified beyond personal, cultural, historical associations as a ¨fox¨ in modern China. The fox image represents an archetype of masculine energy emerged from female agency as the animal instinct to break through tradition and the limitations of culture and gender.

Peningkatan Kemampuan Peserta Didik Kelas XI MA Al-Bairuny Jombang pada Materi Akidah Akhlak Berbantuan Media Sosial TikTok

Increasing students' abilities through Tiktok learning media in material on moral beliefs. The application of TikTok learning media has not been very effective because many irresponsible users display negative content which is likely to be watched by children. Therefore, children's use of social media requires supervision from teachers and parents in learning activities so that technological advances can be used optimally. The purpose of this research is to analyze students' abilities in moral belief material through the Tiktok application, analyze students' needs for Tiktok social media, what material is explained in the Tiktok video content, validator assessment of moral belief content, analyze the increase in students' ability to understand moral belief material. morals through Tiktok social media, and analyzing how much student satisfaction there is with moral aqidah material through Tiktok social media among 11th grade IPS 1 students and 24 11th grade IPS 2 students at MA AL-Bairuny Sambong Dukuh Jombang. The method used in this research is a quantitative method. Meanwhile, data collection used questionnaires and tests with the help of questionnaire instruments and interview techniques which were carried out for approximately six days. The results of the research state that the use of modern learning media is considered effective in learning moral beliefs. Students feel that explaining material using social media Tiktok is easier to understand because it uses videos and animated images that support the learning process which is in line with improving student learning outcomes. Creating creative and innovative moral creed content is a good thing to implement. Making videos through the Tiktok application is considered effective in improving students' abilities.

Cross‐Species Segmentation of Animal Prostate Using a Human Prostate Dataset and Limited Preoperative Animal Images: A Sampled Experiment on Dog Prostate Tissue

ABSTRACTIn the development of medical devices and surgical robot systems, animal models are often used for evaluation, necessitating accurate organ segmentation. Deep learning‐based image segmentation provides a solution for automatic and precise organ segmentation. However, a significant challenge in this approach arises from the limited availability of training data for animal models. In contrast, human medical image datasets are readily available. To address this imbalance, this study proposes a fine‐tuning approach that combines a limited set of animal model images with a comprehensive human image dataset. Various postprocessing algorithms were applied to ensure that the segmentation results met the positioning requirements for the evaluation of a medical robot under development. As one of the target applications, magnetic resonance images were used to determine the position of the dog's prostate, which was then used to determine the target location of the robot under development. The MSD TASK5 dataset was used as the human dataset for pretraining, which involved a modified U‐Net network. Ninety‐nine pretrained backbone networks were tested as encoders for U‐Net. The cross‐training validation was performed using the selected network backbone. The highest accuracy, with an IoU score of 0.949, was achieved using the independent validation set from the MSD TASK5 human dataset. Subsequently, fine‐tuning was performed using a small set of dog prostate images, resulting in the highest accuracy of an IoU score of 0.961 across different cross‐validation groups. The processed results demonstrate the feasibility of the proposed approach for accurate prostate segmentation.

Meningkatkan Kemampuan Berbicara Pada Anak Melalui Vidio Interaktif

Currently, education for early childhood is developing rapidly, which can be seen from the various media used by teachers. This research aims to explore the use of interactive videos as a learning medium in improving the language skills of young children. Interactive videos are considered an innovative and interesting medium for children because they present material with cute animated images, historical folklore and interesting musical instruments. This research uses qualitative methods with a literature study approach. The research results show that learning through interactive videos can increase children's enthusiasm for learning, develop critical thinking skills, and help children be more confident in expressing their opinions on a topic.

ReLU-oscillator: Chaotic VGG10 model for real-time neural style transfer on painting authentication

Neural Style Transfer (NST) has exerted algorithms to generate animation images in computer vision for decades. The Convolution Neural Network (CNN) applied to image content and styles in the NST has improved the extraction of functionalities and the calculation of the convergence speed to recognize and generate high quality structure images, but unpredictable loss elements are inadequate to iterate human learning ability of unique artists’ paintings or styles. This paper offers a chaotic VGG10 NST model based on CNN, ReLU and Lee-Oscillator. The proposed ReLU-Oscillator dynamically relies on activation functions in a chaotic state that dynamically improves high-frequency iterative training of high-quality image and high-speed time optimization. In addition, the best parameters recognizing and determining a personalized painting style from the search for ReLU-Oscillator by corresponding to a set of parameters from proposed Optimal Oscillator Parameter Search Algorithm. Experimental results showed that the stylized image generated by the Chaotic VGG 10 model with high-frequency oscillation succeeded in reducing the training time in magnitude models with the smallest Params and FLOPs in model performance and image quality with the lowest content loss for preserving semantic information and moderate style loss for style similarity balanced with comparison to 8 state-of-the-art models in visual perception evaluation. Chaotic NST has a unique identification for each artist supplemented with a set of oscillator parameters to evaluate the loss performance based on the relative error between the famous painting and its imitation, indicating that the authentication of paintings can be detected through specific ReLU-Oscillator parameters for each stylization, estimated from the loss value performance.

Pelatihan Pembuatan Video Animasi 3 Dimensi Fotosintesis Mata Pelajaran IPA pada Anak Sekolah Dasar

Animated video is an audio-visual medium by combining animated images that can move followed by audio corresponds to the animated character. and in this case the service uses 3-dimensional animation. This community service aims to provide knowledge to children to be able to make 3-dimensional animation videos of photosynthesis in natural science subjects so that children can know and learn easily how the photosynthesis process works, using technology. The research method uses the Luther Sutopo method with the following steps: concept, design, material collection, manufacture, testing and distribution. The initial stage that was carried out was to think about the concept, namely the goal, was to produce a 3-dimensional animation video of photosynthesis through the training carried out, and for whom this training was carried out, namely for children who were still attending elementary school. Next, do a 3-dimensional animation design for the video which is made in the form of text, images, audio, video and animation. Then collect the materials needed to make this animated video such as background, images, sound and other supporting materials. After collecting all the materials, this 3-dimensional animated video was made until final editing. Then a test was carried out on the animated video that was made to see whether it was suitable for use as training material for elementary school children. After being declared suitable, it will be immediately distributed to elementary school children. The impact is that it is hoped that children who are still in elementary school will be technologically literate, so that later they can make other animated videos with different material.

Animal Images in Shamanic Cult and Attributes in Traditional Yakut Culture

The purpose of this article is to systematize and define the role of the animal world in the mythology of Yakut shamanism and its attributes. The topic of shamanism was studied earlier mainly from an anthropocentric point of view. At present, it is interesting to consider the role of animal images in the shamanic cult and the way their communication with the shaman takes place. The sources of the study were archival and published ethnographic and folklore materials on traditional Yakut beliefs. With their help the plots in which animals appear as patron spirits, spirit-helpers and symbolic images were revealed. The animal image of the shamanic patron spirit – “iye-kyyl” (mother-beast) which participates in the “birth” of the shaman was considered. We also paid attention to animals acting as helper spirits directly in contact with the shaman: they ensure his mobility in supernatural spaces and share their powers and abilities with him. The material use of animal images for magical and apotropaic purposes in the shaman’s paraphernalia is touched upon. The methodological basis of the study is the actor-network theory and the symbolic approach. As a result of the study, we conclude that in Yakut shamanism animal images play a significant role of intermediaries between shamans and supernatural spaces. The study reveals the ideas that spirits act in an active capacity: they do not simply carry functional roles, but are themselves the initiators of the supernatural spaces.

Neuronal tuning and population representations of shape and category in human visual cortex

Object recognition and categorization are essential cognitive processes which engage considerable neural resources in the human ventral visual stream. However, the tuning properties of human ventral stream neurons for object shape and category are virtually unknown. We performed large-scale recordings of spiking activity in human Lateral Occipital Complex in response to stimuli in which the shape dimension was dissociated from the category dimension. Consistent with studies in nonhuman primates, the neuronal representations were primarily shape-based, although we also observed category-like encoding for images of animals. Surprisingly, linear decoders could reliably classify stimulus category even in data sets that were entirely shape-based. In addition, many recording sites showed an interaction between shape and category tuning. These results represent a detailed study on shape and category coding at the neuronal level in the human ventral visual stream, furnishing essential evidence that reconciles human imaging and macaque single-cell studies.

PHRASEOLOGICAL UNITS WITH A ZOONYMIC COMPONENT THAT CHARACTERIZE THE CHARACTER OF A PERSON IN THE KAZAKH AND ENGLISH LANGUAGES

The article deals with phraseological units with zoonymic components that characterize the character of a person in the Kazakh and English languages, explores linguistic expressive means using animal images to describe human qualities and behavioral characteristics. In the work, we will review phraseological units with zoonymic components in both languages and analyze them using a comparative method.The purpose of the scientific article is to identify similarities and differences in the description of the peculiarities of the use of phraseological units, analysis of phraseological units with zoonimic components in Kazakh and English languages in the discourse of representatives of the two cultures.The main section of the article proves that phraseological units with zoonymic components are a reflection of cultural characteristics and views on human character in the Kazakh and English languages. The analysis showed that phraseological units with animals to describe human qualities are used in the two languages. Phraseological units in the Kazakh language are closely related to animals, that is zoonyms reffered to livestock, while English language mainly relies on zoonyms related to birds, insects and sea creatures. The results of the study may be useful to linguists, translators and cultural scientists who study the interaction of language and culture.The relevance of the research topic was the comparative analysis of phraseological phrases with zoonymic components in the Kazakh and English languages, revealing similarities and differences in the way, how human character is perceived and described by the different representatives of cultures. The authors presented their own vision of the problems based on the analysis of the works of the scientists on this topic. Comprehensive analysis of phraseological units with zoonymic components, which have become the main object of the article's research were carried out.

Review of researches and technologies applicable to digitalization of the process of assessing the exterior of meat and dairy animals

To increase the efficiency of livestock farming, scientists are developing information and communication technologies aimed at digitalizing the process of assessing the exterior of animals. This review should improve understanding of the development steps of systems applicable to the digitalization of animal conformation assessment using computer vision and deep learning neural networks. The search focused on several topics: computer vision systems; training datasets; image acquisition systems; deep learning models; neural networks for training; performance parameters and system evaluation. Machine vision is an innovative solution by combining sensors and neural networks, providing a non-contact way to assess livestock conditions as cameras can replace human observation. Two approaches are used to obtain three-dimensional images for digitalization tasks in animal husbandry: shooting animals using one 3D camera fixed in one place, and shooting from different points using several 3D cameras that record images of animals and individual parts of their bodies, such like an udder. The features extracted from the images, called dorsal features, are used as input to the models. The reviewed publications used a variety of deep learning models, including CNN, DNN, R-CNN, and SSD, depending on the task. Similarly, neural networks such as EfficientNet, ShapeNet, DeepLabCut and RefineDet have been mainly used for animal health monitoring, while GoogleNet, AlexNet, NasNet, CapsNet, LeNet and ERFNet are mainly used for identification purposes.

A novel DbneAlexnet with Gazelle Hunting Optimization Algorithm enabled wild animal detection in WMSN data communication in IoT environment

SummaryThe main goal of this strategy is to detect the occurrence of wild animals in farmsteads and human habitats using an improved deep learning in order to avoid loss of life and damage to agricultural crops. This paper formulates a technique for recognizing wild animals in an Internet of Things (IoT) environment utilizing deep batch normalized exponential linear unit AlexNet (DbneAlexnet) and the Gazelle Hunting Optimization Algorithm (GHOA). The IoT‐Multimedia Sensor Networks (WMSN) network is first simulated, with the IoT nodes capturing the images needed for wild animal identification. The suggested GHOA is used for routing, which sends the captured images to the base station (BS). The input wild animal image is transmitted to image preprocessing at the BS, where it is processed using the Weiner filter (WF) to remove undesirable noise from the image. The denoised output is sent into salient map extraction, which extracts salient map to determine the regions that are conspicuous or noticeable at every place in the field of vision and guides the selection of attended location. Finally, the saliency map is forwarded to DbneAlexnet for detecting the wild animals, where the DbneAlexnet is trained using proposed GHOA. The suggested GHOA algorithm is created by combining the Gazelle Optimization Algorithm (GOA) and the deer hunting optimization algorithm (DHOA). Furthermore, detection is appraised by precision, recall, and f1‐score, which provide values of 90.2%, 89.1%, and 89.6%, respectively.

An analysis of information segregation in parallel streams of a multi-stream convolutional neural network.

Visual information is processed in hierarchically organized parallel streams in the primate brain. In the present study, information segregation in parallel streams was examined by constructing a convolutional neural network with parallel architecture in all of the convolutional layers. Although filter weights for convolution were initially set to random values, color information was segregated from shape information in most model instances after training. Deletion of the color-related stream decreased recognition accuracy of animate images, whereas deletion of the shape-related stream decreased recognition accuracy of both animate and inanimate images. The results suggest that properties of filters and functions of a stream are spontaneously segregated in parallel streams of neural networks.

Virtual Reality Animation Interaction Design using Bayesian Physics-Informed Neural Network with Archimedes Optimization Algorithm Based Scene Modelling

Virtual Reality Animation Interaction Design is a dynamic field at the intersection of technology and creativity, where immersive experiences come to life through the fusion of animation and interactive design. There are some challenges involves in design of virtual Reality animation. The main challenge is animation image error involves in the design. To overcome this issue, present a Virtual Reality Animation Interaction Design Using Bayesian Physics-Informed Neural Network with Archimedes Optimization Algorithm Based scene modelling (VRAID-BPINN-AOA) is proposed. Initially, the animation images are collected from animation dataset. Then, the animation images are fed to pre-processing segment. In pre-processing segment, the noise of the image is removing layer by layer by utilizing adaptive-noise Augmented Kalman Filter (ANAKF). Then the pre-processed animation image is given for Feature extraction process. In Feature extraction, spatiotemporal features such as Object Position, velocity, Optical flow and Crowd Density are extracted by utilizing Multi-Level 2-D Quantum Wavelet Transform (ML2DQWT). Finally the extracted feature attributes are given to Bayesian physics-informed neural network (BPINN) for the prediction of error in the animation images. In general, BPINN does not express some adaption of optimization strategies for determining optimal parameters to promise accurate prediction of error. Therefore, Archimedes Optimization Algorithm (AOA) is proposed to optimize the parameter of BPINN .The proposed technique is implemented and efficacy of VRAID-BPINN-AOA technique is assessed by support of numerous performances such as Bit Error Rate, Design Rate, End to End Delay, Latency, and Transmission Rate. Proposed VRAID-BPINN-AOA method attains 28.56%, 26.67% and 25.67% lower Bit error rate and26.42%, 25.67% and 23.67% lower Design rate are compared with existing such as Virtual Interactive Animation Design with Restricted Boltzmann machine (VI-AD-RBM),Generative deep learning for visual animation in landscapes design using Generative adversarial network(GDL-VALD-GAN), and Hand interface using deep learning in immersive virtual reality using convolutional neural network(HID-LVIR-CNN)respectively.