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Dealing with disasters is typically illustrated with photos of the aftermath of disasters, notably collapsed infrastructure and destitute, hurt, or grieving people. Those images present the reality of disasters, but not the reality of actions seeking to avoid these scenes. This photo essay is of disaster risk reduction efforts as expressed through images. [ABSTRACT FROM AUTHOR]

ardiovascular imaging has become the foundation of heart failure diagnostic studies. The most crucial technique for clinically diagnosing cardiac diseases is echocardiography. Depending on the positioning and angles of the probe, different cardiac views can be obtained during echocardiography. Therefore, the automatic classification of echo views, especially for computer systems and even automatic diagnosis in later stages, is the first step for echocardiogram diagnosis. In addition, the classification of heart views allows the tagging of echo videos to be done on a high scale and the possibility of database management and collection is provided. However, deep learning is an advanced machine learning method that is used to analyze both natural and medical images. But so far, it has not been widely used on cardiac ultrasound, the reason is the complexity of formats with multiple views and multi-view formats of echocardiogram. The proposed topic of this research is to provide novel and effective architectures for cardiac view classification. The aim of this study is to overcome the challenges in processing, categorizing and recognizing echo views stored as videos and images. In particular, in order to extract features, automatic methods and deep networks have replaced manual methods. In the presented solution, by using the transfer learning and the 3d-cnn method in image and video classification, we have improved the accuracy of echo views classification. [ABSTRACT FROM AUTHOR]

In traditional digital filing systems, people mostly use text as a key to categorise images, and retrieve them in the future. The use of other media as keys for image retrieval is rarely used, notwithstanding that multisensory digital media – mulsemedia – can be harnessed to improve users' performance and help them to retrieve their images. In this respect, olfactory media (engaging the sense of smell) is an example, as people can categorise their images by using congruent olfactory media. Accordingly, we investigated the impact of employing olfactory media as a key for retrieving a set of images. Moreover, we also studied the impact of the usage of olfactory media in this context on a user's performance and Quality of Experience (QoE). To this end, we developed an olfactory-enhanced application (SCENT2IMAGE) in which olfactory media was emitted alongside a 5X5 matrix of images, of which users had to recognize 4 images congruent with the emitted scents. Furthermore, we developed a word-only version of the application (WORD2IMAGE) in which words alone were used as an equivalent key instead of olfactory media. Forty-four participants were invited and took part in our experiment, evenly split into a control and experimental group. Results highlight that using olfactory media does have a significant impact on user performance by helping them find related images. Moreover, using olfactory effects in this context was also found to enhance user QoE. Lastly, our findings underscore that users were willing to use olfactory-enhanced applications for categorizing/retrieving their albums and images. [ABSTRACT FROM AUTHOR]

During national crises, political elites often rally around the flag, promoting a central message to restore unity and calm the public. COVID-19 provided such a crisis. But did elites rally? The pandemic occurred at a point of extreme polarization in the United States, which threatens the potential for a rally. In this article, we argue that messaging about masking during COVID-19 offers an opportunity to test the comparative effects of a rally versus polarization. To do so, we use a unique measure: visual public communication by members of Congress (MOCs). We extract 340,000 images from congressional Twitter and Facebook accounts and employ supervised machine-learning methods to identify when MOCs posted images of people wearing masks. We find evidence of both rally effects and polarization. Trump's actions are especially important: while Trump-loyal Republicans are less likely to post masks, all Republicans increased posting masks after Trump first appeared wearing a face mask. [ABSTRACT FROM AUTHOR]

This article explores how universities construe organizational identities and engage digital audiences through images on web homepages. Combining visual content analysis and a discourse-analytic approach informed by social semiotics, I interpret the discourses of identity in 400 images from organizational homepages of four top-tier public universities in Sydney, Australia – University of Sydney, University of New South Wales, University of Technology Sydney, and Macquarie University. Based on the social semiotic interpretation of images, I identify eight identity icons, each deploying a combination of semiotic resources to represent a specific organizational identity. The analysis suggests that universities prioritize featuring people, which results in an augmented sense of social presence on the homepage. Lastly, four identified strategies for digital audience engagement in images – proximation, alignment, equalization, and subjectivation – point to how these are instrumental in representing university life as both individual and shared experiences. [ABSTRACT FROM AUTHOR]

Objective: To investigate the consistency of Chatbot Generative Pretrained Transformer (ChatGPT)‐4 in the analysis of clinical pictures of common laryngological conditions. Study Design: Prospective uncontrolled study. Setting: Multicenter study. Methods: Patient history and clinical videolaryngostroboscopic images were presented to ChatGPT‐4 for differential diagnoses, management, and treatment(s). ChatGPT‐4 responses were assessed by 3 blinded laryngologists with the artificial intelligence performance instrument (AIPI). The complexity of cases and the consistency between practitioners and ChatGPT‐4 for interpreting clinical images were evaluated with a 5‐point Likert Scale. The intraclass correlation coefficient (ICC) was used to measure the strength of interrater agreement. Results: Forty patients with a mean complexity score of 2.60 ± 1.15. were included. The mean consistency score for ChatGPT‐4 image interpretation was 2.46 ± 1.42. ChatGPT‐4 perfectly analyzed the clinical images in 6 cases (15%; 5/5), while the consistency between GPT‐4 and judges was high in 5 cases (12.5%; 4/5). Judges reported an ICC of 0.965 for the consistency score (P =.001). ChatGPT‐4 erroneously documented vocal fold irregularity (mass or lesion), glottic insufficiency, and vocal cord paralysis in 21 (52.5%), 2 (0.05%), and 5 (12.5%) cases, respectively. ChatGPT‐4 and practitioners indicated 153 and 63 additional examinations, respectively (P =.001). The ChatGPT‐4 primary diagnosis was correct in 20.0% to 25.0% of cases. The clinical image consistency score was significantly associated with the AIPI score (rs = 0.830; P =.001). Conclusion: The ChatGPT‐4 is more efficient in primary diagnosis, rather than in the image analysis, selecting the most adequate additional examinations and treatments. [ABSTRACT FROM AUTHOR]

In the present situation human health is a very important factor. With today's growing technology, health conditions are deteriorating day by day. Malnutrition, lifestyle play a very important role. These are leading to many diseases such as respiratory infections, heart disease, diabetes, Cancer, Alzheimer's, strokes and many more. Among these, few diseases are curable, due to the facilities available. Whereas few may be non-curable if care is not taken in the initial stages. Cancer poses the biggest threat among all life-threatening diseases. An efficient lesion segmentation method is introduced for the detection of Melanoma skin cancer disease at the preliminary stages using Adaptive Contour Model (ACM). High-quality segmentation is achieved based on contour features and sharp edge detection using ACM. The performance of the proposed Adaptive Contour Model (ACM) is tested upon PH2 and ISIC Challenge 2017 Dataset. The Performance matrices for the segmentation process are measured in terms of Jaccard index (JA), Dice coefficient (DI) and accuracy that are found to be 79.23, 87.26 and 94.63 considering ISIC dataset and 89.14, 93.98 and 96.95 which is quite high considering PH2 dataset. A method for detection of melanoma has been the critical need of the day. The proposed method shows that the performance of the Adaptive Contour Model (ACM) is more than 95%, which is better than other methods. [ABSTRACT FROM AUTHOR]

In Human Nature, Hobbes argues for what I call the ‘Great Deception Thesis': “whatsoever accidents or qualities our senses make us think there be in the world, they are not there, but are seemings and apparitions only”. I argue that both the thesis and Hobbes’ arguments for it have been misunderstood. Rather than arguing for indirect realism or a primary/secondary quality distinction, Hobbes claims that no sensory experience resembles its object. I conclude by showing how Hobbes can account for the usefulness of images even in the absence of any resemblance between those images and the objects that cause them. If I am right, Hobbes presents a distinctive and intriguing theory of perception, one that emphasizes the dynamic nature of experience. [ABSTRACT FROM AUTHOR]

Brain tumor (BT) is an awful disease and one of the foremost causes of death in human beings. BT develops mainly in 2 stages and varies by volume, form, and structure, and can be cured with special clinical procedures such as chemotherapy, radiotherapy, and surgical mediation. With revolutionary advancements in radiomics and research in medical imaging in the past few years, computer-aided diagnostic systems (CAD), especially deep learning, have played a key role in the automatic detection and diagnosing of various diseases and significantly provided accurate decision support systems for medical clinicians. Thus, convolution neural network (CNN) is a commonly utilized methodology developed for detecting various diseases from medical images because it is capable of extracting distinct features from an image under investigation. In this study, a deep learning approach is utilized to extricate distinct features from brain images in order to detect BT. Hence, CNN from scratch and transfer learning models (VGG-16, VGG-19, and LeNet-5) are developed and tested on brain images to build an intelligent decision support system for detecting BT. Since deep learning models require large volumes of data, data augmentation is used to populate the existing dataset synthetically in order to utilize the best fit detecting models. Hyperparameter tuning was conducted to set the optimum parameters for training the models. The achieved results show that VGG models outperformed others with an accuracy rate of 99.24%, average precision of 99%, average recall of 99%, average specificity of 99%, and average f 1-score of 99% each. The results of the proposed models compared to the other state-of-the-art models in the literature show better performance of the proposed models in terms of accuracy, sensitivity, specificity, and f 1-score. Moreover, comparative analysis shows that the proposed models are reliable in that they can be used for detecting BT as well as helping medical practitioners to diagnose BT. [ABSTRACT FROM AUTHOR]

It is commonly assumed that images, whether in the world or in the head, do not have a privileged analysis into constituent parts. They are thought to lack the sort of syntactic structure necessary for representing complex contents and entering into sophisticated patterns of inference. I reject this assumption. "Image grammars" are models in computer vision that articulate systematic principles governing the form and content of images. These models are empirically credible and can be construed as literal grammars for images. Images can have rich syntactic structure, though of a markedly different form than sentences in language. [ABSTRACT FROM AUTHOR]

In this article, I explore the use, production, and readings of images in antiracist work as processes in constant tension with racist logics. Racist logics are deceptive and entrapping, and the visible world is a particularly dynamic arena in which to analyse this. Images, specifically photographs, incorporate a nuanced and productive contradiction between their ability to illustrate, explain and evoke, on the one hand, while also ensnaring, on the other, partly through alluring and providing pleasure. This contradiction means that anti-racist projects may include the possibility of re-inscribing racist discourse and practice. Drawing on empirical examples from Mexico, this article interrogates how photographic representations of Blackness have been entangled with the racist logics and racial projects that feed them. Racialised images that emphasise the physical body of Black people raise questions about how mestizaje, Latin America's main racial formation, has coded the visual space of representation in confusing ways. [ABSTRACT FROM AUTHOR]

The paper presents findings from a study that examined, through the lens of the Image Having layer of the Pirie–Kieren model, the qualitative characteristics of the images that different children formed when engaging with eight, novel partitive quotient tasks. The Image Having layer is the first point of abstraction within the Pirie–Kieren model. Therefore, this research is significant in aiming to advance theoretical insight into how the notion of child-created images relates to the development of children's mathematical understanding, in the context of novel for them tasks. This study adopted a qualitative, microgenetic research design and involved nine Year 5 (aged 9–10 years) children. Data based on children's verbal responses and jottings were collected through multiple trials over eight individual sessions with each child. Analysis of 72 interview transcripts showed that children formed and used a range of different images that varied across tasks but also within the same task. This study provides a nuanced description of qualitative distinctions in the nature of child-created images. It thus reveals varied dimensions of a dynamic process of knowledge development and sense-making. This highlights, for educators, the need to be aware of and adaptive to the varied and dynamic dimensions of knowledge that children draw from, when dealing with novel tasks, and which change as children's understanding of new mathematical content develops. [ABSTRACT FROM AUTHOR]

Corrosion negatively impacts the functionality of civil structures. This paper introduces a new methodology that combines the fractality of vibration signals with a data processing stage utilizing autoencoders to detect corrosion damage in a truss-type bridge. Firstly, the acquired vibration signals are analyzed using six fractal dimension (FD) algorithms (Katz, Higuchi, Petrosian, Sevcik, Castiglioni, and Box dimension). The obtained FD values are then used to generate a gray-scale image. Then, autoencoders analyze these images to generate a damage indicator based on the reconstruction error between input and output images. These indicators estimate the damage probability in specific locations within the structure. The methodology was tested on a truss-type bridge model placed at the Vibrations Laboratory from the Autonomous University of Queretaro, Mexico, where three damage corrosion levels were evaluated, namely incipient, moderate, and severe, as well as healthy conditions. The results demonstrate that the proposal is a reliable tool to evaluate the condition of truss-type bridges, achieving an accuracy of 99.8% in detecting various levels of corrosion, including incipient stages, within the elements of truss-type structures regardless of their location. [ABSTRACT FROM AUTHOR]

If images were once conceived either as subordination or oppositions of words, the advent of the Internet has started new relationship dynamics between them. Within this digital scenario, vision and textuality intensely intertwine in the form of captions, posts, hyperlinks or other kind of social media content, daily handled by users. In this context, autobiographical narratives will take on a major role, shaping the way in which the subject conceives, forges and registers his or her identity in relation to the reader/viewer. This article aims to address the question, more present than ever, of how certain autobiographical narratives are disseminated through social networks, and how image and narrative dialogue within this framework. By addressing specific cases found on platforms such as Instagram, we will try to elucidate how these publications point to an ambivalent relationship with visual tropes or narratives associated with certain life experiences, either questioning it or contributing to the creation of a collective narrative. A complex interplay of image and narrative that will affect not only how we share life, but also how we understand it and construct it together with others in a changing world.

This article looked at how the ‘Ghanaian Times’ newspaper, using stories sourced from the British Broadcasting Corporation (BBC), framed Africa to the rest of the world. The study which utilized the mixed approach also employed the gatekeeping, framing and cultural imperialist theories in its attempt to find out which of the regional blocs in Africa dominated the coverage and whether the portrayal was positive or negative. The study found that the majority of the stories that the ‘Ghanaian Times’ sourced from the BBC were negative about Africa hence framing Africa as an unhealthy place for living. Out of the 154 stories the newspaper sourced from the BBC, only 52 stories focused on the positive happenings in Africa while 94 stories painted a catastrophic image about Africa. Also, political unrest and crime were the two dominant themes Africa was associated with. In all, the general image of Africa as portrayed by the newspaper was negative as the editors focused more on negative stories of Africa compared to the positives amidst the abundance of stories published by the BBC. This arguably means that Africa’s negative image could only be corrected if editors are deliberate in selecting more positive stories about Africa for publication. This work will indeed add to the literature on the framing of Africa in the media especially the contribution of the African media to the dominant negative image Africa continues to be associated with.

In approximation theory, classical discrete operators, like generalized sampling, Szász‐Mirak'jan, Baskakov, and Bernstein operators, have been extensively studied for scalar functions. In this paper, we look at the approximation of curves by a class of discrete operators, and we exhibit graphical examples concerning several cases. The topic has useful implications about the computer graphics and the image processing: We discuss applications on the approximation and the reconstruction of curves in images. [ABSTRACT FROM AUTHOR]

Increased urbanization coupled with climate change is increasing the number and intensity of stormwater flooding events. Implementing efforts to successfully manage stormwater flooding depends on understanding how people perceive these events. While images of stormwater flooding abound, how these images influence perceptions about flooding events or management options remains understudied. Our objective is to contribute to the general understanding of how various types of images depicting stormwater runoff and stormwater related flooding influence individual and group interpretations of causes of events, major impacts of those events, and responsibility for managing stormwater related events. To this end, we convened focus groups, gave participants numerous photos of stormwater flooding, asked them to identify which images were most concerning, and to then discuss the specific aspects of the photos that prompted concern. We also tested whether a priming image implicating climate change or development as a cause of stormwater flooding influenced viewer reactions. Finally, we asked participants about preferences for who should manage stormwater. Our results revealed that photo location, the water's appearance, and what people were doing in the photo influenced levels of concern. We also found that priming seems to affect opinions regarding urban stormwater management. Finally, there is some evidence that the absence of people in the photo may affect beliefs about who should manage stormwater. [ABSTRACT FROM AUTHOR]

The role that visual discriminative ability plays among giant pandas in social communication and individual discrimination has received less attention than olfactory and auditory modalities. Here, we used an eye-tracker technology to investigate pupil fixation patterns for 8 captive male giant pandas Ailuropoda melanoleuca. We paired images (N =  26) of conspecifics against: 1) sympatric predators (gray wolves and tigers), and non-threatening sympatric species (golden pheasant, golden snub-nosed monkey, takin, and red panda), 2) conspecifics with atypical fur coloration (albino and brown), and 3) zookeepers/non-zookeepers wearing either work uniform or plain clothing. For each session, we tracked the panda's pupil movements and measured pupil first fixation point (FFP), fixation latency, total fixation count (TFC), and duration (TFD) of attention to each image. Overall, pandas exhibited similar attention (FFPs and TFCs) to images of predators and non-threatening sympatric species. Images of golden pheasant, snub-nosed monkey, and tiger received less attention (TFD) than images of conspecifics, whereas images of takin and red panda received more attention, suggesting a greater alertness to habitat or food competitors than to potential predators. Pandas' TFCs were greater for images of black-white conspecifics than for albino or brown phenotypes, implying that familiar color elicited more interest. Pandas reacted differently to images of men versus women. For images of women only, pandas gave more attention (TFC) to familiar combinations (uniformed zookeepers and plain-clothed non-zookeepers), consistent with the familiarity hypothesis. That pandas can use visual perception to discriminate intra-specifically and inter-specifically, including details of human appearance, has applications for panda conservation and captive husbandry. [ABSTRACT FROM AUTHOR]

Sustainability reporting has been established as a dominant but problematic communication practice in global sustainability management. In this paper, we challenge the commonplace view of reporting as the simple and textual transmission of information, by interpreting corporate sustainability reports as visual artefacts that encourage particular views of environmental issues. We discuss visualization as a key practice in communication and rhetoric, asking what visual managerialism looks like in corporate reports, focusing on a corpus drawn from the Swedish United Nations Global Compact. We engage in a three-stage rhetorical critique to identify three kinds of visualizations: numerical, diagrammatic, and pictorial, establishing how they emphasize communicative characteristics such as logic, simplicity, and clarity. We consolidate these characteristics with the term 'Powerpoint Imagination,' arguing that they construct environmental problems in terms of a technocratic solutionism, i.e., efficiency, standardization and control. We discuss some issues with the Powerpoint Imagination by contrasting it with other images from sustainability reports, concluding with implications for future research. [ABSTRACT FROM AUTHOR]

Images are today at the centre of multiple social and technological tensions as a consequence of the adoption of digital coding, of the massive diffusion of social networks and of the algorithmic processing to which they are subject, resulting in new opportunities for developing analytical inquiries and meaning-producing social actions. In this introduction, the authors intend to reconstruct the broad context that makes images one of the most important resources of the digital era and to focus on some of the research tracks that characterize it. In the first part, they begin by focusing on the relationship between images and the digital, which they retrace in accordance with the selection of three key moments: the transition from ontology to the epistemology of digital media; the opening, by social networks and portable devices, of a field for the computational study of contemporary cultures; and, finally, the analytical potential arising from the encounter between digital archives and computer algorithms. In the second part, they present the three axes around which this issue is structured: archives, identity and algorithms. They first of all discuss the concept of the archive, by presenting four different understandings it has come to bear in conjunction with digital encoding – the archive as heritage, resource, effect and as database. They go on to address the relation between images and identities, arguing that social platforms and visual apps are a new domain for identity experimentation and social aggregation. Finally, they discuss the issue of algorithms and more generally of the new computational economy that associates large amounts of data with their mobilization as operational images. [ABSTRACT FROM AUTHOR]

English as a Foreign Language (EFL) textbook image resources provide important materials and ways of learning for EFL students. Previous studies on the meaning and function of images in Chinese EFL textbooks tend to take pedagogy and cognitive psychology as main perspectives. Based on visual grammar, this article analyzes the image resources in Chinese junior high school EFL textbooks from a multimodal perspective, attempting to explore how the images realize three meta-meanings. It is found that cartoon, photo, table, and diagram are four main types of images in Yilin EFL textbooks. Quantitative analysis and qualitative analysis show that the images in the textbooks realized the representational meaning, interactive meaning and compositional meaning, but there are still some problems in image design, such as insufficient image types, lack of images promoting knowledge comprehension, and inadequate realization of compositional meaning of images. The research results offer some implications for Chinese EFL textbook editors to design textbooks, and also provide some suggestions for Chinese EFL teachers' teaching practice and students' learning. [ABSTRACT FROM AUTHOR]

A novel approach that combines segmented fundus images (FIs) and optical coherence tomography image (OCTIs) are presented here, by incorporating deep learning network (DLN) techniques, to address the imperative need for advanced diagnostic algorithms in detecting and classifying glaucoma. By combining these two images, glaucoma diagnoses are made to improve the accuracy with more reliability. Multi modal convolutional neural networks (MMCNNs) are proposed for automatically extracting discriminatory features from both segmented FIs and OCTIs, allowing for comprehensive ocular analysis. A significant improvement in glaucoma diagnosis is achieved through segmentation of both FIs and OCTIs, ensuring robustness generalization to diverse clinical scenarios, DLN models are trained on datasets encompassing a wide range of glaucoma cases. The integrated approach outperforms individual modalities in terms of early detection of glaucoma and accurate classification. This method demonstrates promising potential in early glaucoma detection due to its effectiveness. By combining segmented features from both FIs and OCTIs through MMCNNs, improved efficiency in diagnosing predominant ocular glaucoma disorder is achieved compared to existing methods. Within the scope of this research, GoogLeNet (GN) is applied to independently classify glaucoma (uni-modal) in segmented FIs and OCTIs, providing a basis for comparison with the evaluation of MMCNNs. [ABSTRACT FROM AUTHOR]

AbstractDespite increasing attention to images in the study of world politics, the role of visual representations in transnational governance processes in general and migration governance, in particular, has received less attention. This paper aims to fill this gap by examining the visual representation of migration governance by the two Canadian government institutions responsible for it: Immigration, Refugees and Citizenship Canada (IRCC) and the Canada Border Services Agency (CBSA). Through a multi-modal analysis of their X (formerly Twitter) images, the paper shows how they tend to shy away from a visual representation of people on the move, privileging a technical communication in an aspiration toward a “neutral” representation of migration issues. Secondly, it sheds light on the discrepancies between the policies directed toward Afghani and Ukrainian refugees and the unexpectedly undifferentiated visual communication about the two groups. Finally, the paper explores what the pictures accompanying X (formerly Twitter) posts of IRCC and CBSA can tell about how these government institutions represent themselves. Findings showed that government institutions’ esthetic practices are not always in line with the institutional migration policies and text narratives, suggesting that different government implementation tools (i.e., regulations, practices, textual discourses) may not reinforce each other but advance alternative perspectives. [ABSTRACT FROM AUTHOR]

Research on political conflict often overlooks the role of visual-based platforms like Instagram in expressing political discontent, focusing primarily on textual content from newspapers and social media. This paper examines the practicalities and challenges of conducting visual research on Instagram, particularly in the context of comparative studies. We highlight the difficulties associated with sampling representative visual content. Through a small case study, we illustrate how hashtags associated with a single country can generate multiple conflicts, using indicators developed in political protest research and contentious politics. The existence of diverse debates within and across hashtags complicates cross-country comparisons of Instagram content and conflict dynamics. To address this issue, we propose an analytical tool for cross-hashtag analysis, allowing for the assessment of degrees of conflict. [ABSTRACT FROM AUTHOR]

This article uses critical discourse analysis to investigate artificial intelligence (AI) generated images of aged care nurses and considers how perspectives and perceptions impact upon the recruitment and retention of nurses. The article demonstrates a recontextualization of aged care nursing, giving rise to hidden ideologies including harmful stereotypes which allow for discrimination and exploitation. It is argued that this may imply that nurses require fewer clinical skills in aged care, diminishing the value of working in this area. AI relies on existing data sets, and thus represent existing stereotypes and biases. The discourse analysis has highlighted key issues which may further impact upon nursing recruitment and retention, and advocates for stronger ethical consideration, including the use of experts in data validation, for the way that aged care services and nurses are depicted and thus valued. [ABSTRACT FROM AUTHOR]

Ischemic brain strokes are severe medical conditions that occur due to blockages in the brain's blood flow, often caused by blood clots or artery blockages. Early detection is crucial for effective treatment. This study aims to improve the detection and classification of ischemic brain strokes in clinical settings by introducing a new approach that integrates the stroke precision enhancement, ensemble deep learning, and intelligent lesion detection and segmentation models. The proposed hybrid model was trained and tested using a dataset of 10,000 computed tomography scans. A 25-fold cross-validation technique was employed, while the model's performance was evaluated using accuracy, precision, recall, and F1 score. The findings indicate significant improvements in accuracy for different stages of stroke images when enhanced using the SPEM model with contrast-limited adaptive histogram equalization set to 4. Specifically, accuracy showed significant improvement (from 0.876 to 0.933) for hyper-acute stroke images; from 0.881 to 0.948 for acute stroke images, from 0.927 to 0.974 for sub-acute stroke images, and from 0.928 to 0.982 for chronic stroke images. Thus, the study shows significant promise for the detection and classification of ischemic brain strokes. Further research is needed to validate its performance on larger datasets and enhance its integration into clinical settings. [ABSTRACT FROM AUTHOR]

In this piece I reflect on Christoforos Bouzanis' book, Social Imaginary and the Metaphysical Discourse , discussing its main contribution to the issue of social science's relation to philosophy and examining Bouzanis' concept of social imaginary as an imagery, raising questions of sense. [ABSTRACT FROM AUTHOR]

With the proliferation of the use of the Internet and social media, the digital media industry has grown enormously in recent years. However, this has brought some challenges, including issues of content security and copyright violation. In this paper, we propose a fusion-based secure watermarking algorithm that we have named "FuSIW". This uses a hashing scheme, to guarantee copyright protection and authentication of RGB images. The algorithm uses the non-subsampled contourlet transform (NSCT) to create a fused watermark image. This contourlet transform (CT) and randomised-singular-value-decomposition (RSVD) based approach allows concealment of the encrypted fused watermark image in the blue channel of the cover image. Subsequently, the hash value of the cover image is inserted into the green channels of the host image. Experimental evaluation indicates that the FuSIW algorithm provides security from geometric attacks and several other common forms of attack. Simulations indicate that the proposed system exhibits improved robustness and security compared to existing methods. [ABSTRACT FROM AUTHOR]

Similarly to China, Japan has a long history of worshiping fortune gods. The act of making offerings and praying to these deities has been practiced since ancient times. Fortune gods are figures in Japanese folk religion that are believed to bring happiness, hope, and good luck. When speaking of fortune gods in Japan, people will first think of the Seven Lucky Gods. Apart from them, there are also some local fortune gods such as Fukusuke and Sendai Shiro. These gods share some common traits and also have connections with the Japanese folk belief in Fukuko (fortune child). This study adopts a comparative methodology to compare Japan's Seven Lucky Gods with the local Japanese fortune gods as well as Fukuko, and then analyze their similarities. This article argues that the Japanese fortune gods have two major common characteristics: the super power to bring good fortune, and their distinctive appearance. By systematically analyzing the common features of Japanese fortune gods, this study will clarify the mechanism behind their deification as fortune deities and also help us to gain a better insight into the Japanese conceptions of deities and spirits. [ABSTRACT FROM AUTHOR]

This article explores some issues related to the trauma of slavery, colonialism and identity-formation processes through a multimodal analysis, which is particularly useful in studying the manifold meanings and relationships emerging from the forms of cultural and social representation such as visual art. A series of images, chosen within Ellen Gallagher's art exhibition "AXME", held at Tate Gallery in London, is investigated to deal with the discursive constructions of gendered and racial identities. Employing Kress and van Leeuwen's model (2021 [1996]. Reading images: The grammar of visual design. London: Routledge), this article attempts to identify the signs related to categories such as gender and race, as well as the persistence of stereotypical representations in our contemporary societies. [ABSTRACT FROM AUTHOR]

The visualization method applies to many scientific fields, allowing you to present information, data, objects and situations as a visual image to incorporate the information as soon as possible. The method of visualization is by and large effective in working with originally "non--visual" information. It is also valuable while representing mental processes, for example feeling and emotions, as well as social and psychological phenomena (e.g. relationships, data about other people) which are sometimes difficult to express verbally. Visualization is always based on mental activity and developed cognitive interest. Visualization in the program of psychocorrection of anxiety disorders of managers, as a psychotechnical technique, combines relaxation, concentration and the construction of an entire imaginative space -- a metaverse of images. The article contains the results of a study aimed at the possibility of using a program for psychocorrection of anxiety disorders for managers under 30 years of age based on visualization. The program was tested on managers under the age of 30, who exhibited high-level personal and situational irritability, anxiety, depression, asthenia, neuroticism, and aggressiveness. All personal parameters decreased significantly after participating in the program. The study confirmed the assumption that there are differences between the manifestation of anxiety of managers younger than 30 years after a correction program based on visualization. [ABSTRACT FROM AUTHOR]

When images of children in conflict situations are selected for a Western audience, what roles do the images fulfill for the audience? A content analysis of photographs provided by the Associated Press of children in Rwanda and Kosovo suggests that news agencies frame children of conflicts differently, as passive agents or success stories, in accordance with ideological and organizational guidelines. The findings of this study show that the Associated Press depicts children in Rwanda in racially stereotypical ways in comparison to their Kosovar counterparts. The current research examines the ways that news media depict children of color in the context of war and conflict. [ABSTRACT FROM AUTHOR]

This paper addresses the classification of images depicting the eruptive activity of Mount Etna, captured by a network of ground-based thermal cameras. The proposed approach utilizes Convolutional Neural Networks (CNNs), focusing on pretrained models. Eight popular pretrained neural networks underwent systematic evaluation, revealing their effectiveness in addressing the classification problem. The experimental results demonstrated that, following a retraining phase with a limited dataset, specific networks such as VGG-16 and AlexNet, achieved an impressive total accuracy of approximately 90 % . Notably, VGG-16 and AlexNet emerged as practical choices, exhibiting individual class accuracies exceeding 90 % . The case study emphasized the pivotal role of transfer learning, as attempts to solve the classification problem without pretrained networks resulted in unsatisfactory outcomes. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence (AI), especially deep learning models, has increasingly been integrated into diagnosing and treating diabetic retinopathy (DR). From delving into the singular realm of ocular fundus photography to the gradual development of proteomics and other molecular approaches, from machine learning (ML) to deep learning (DL), the journey has seen a transition from a binary diagnosis of “presence or absence” to the capability of discerning the progression and severity of DR based on images from various stages of the disease course. Since the FDA approval of IDx-DR in 2018, a plethora of AI models has mushroomed, gradually gaining recognition through a myriad of clinical trials and validations. AI has greatly improved early DR detection, and we’re nearing the use of AI in telemedicine to tackle medical resource shortages and health inequities in various areas. This comprehensive review meticulously analyzes the literature and clinical trials of recent years, highlighting key AI models for DR diagnosis and treatment, including their theoretical bases, features, applicability, and addressing current challenges like bias, transparency, and ethics. It also presents a prospective outlook on the future development in this domain.

Background. Research into the formation of algorithms for obtaining MRI images is highly relevant. The purpose of the work is to analyze advances in the development of a software algorithm for obtaining MRI images of the brain. Materials and methods. Found 17 articles in specialized databases Cyberleninka, eLibrary, PubMed, ScienceDirect. Methods of analysis, synthesis and induction were used. Results. The best way to suppress noise in brain MRI images is the Gaussian Filter, the improvement of which has been achieved through the evolution of neural networks. Automatic segmentation achieved performance comparable to manual segmentation by using a combined system with integrated modules to eliminate the influence of noise and background, to identify image features and edge information. The Sobel operator allows the bright edges of an MRI image to be more clearly identified for removal. For volumetric visualization of brain images, due to its labor-intensive nature, the use of layer-by-layer representation of MRI data is proposed. Watershed segmentation and the K-nearest neighbor classification algorithm resulted in an MRI image accuracy of only 89 %; the wavelet transform was performed without calculating the accuracy. Support Vector Machine (SVM) using the GLCM algorithm showed an accuracy of up to 93 %, but only 36 images were used for training. Based on 150 MRI images of the brain, their classification was performed using the MATLAB 2018a software package (Matrix Laboratory) and a testing accuracy of 96,7 % was achieved. Conclusions. Improved algorithms for removing noise and bright edges from MRI brain images, segmenting them, and creating them with volumetric visualization are being created, including effective software modules for automatic segmentation based on convolutional neural networks.

We were invited by Gitanjali Dang and Christopher Schenker to participate in the Draft Project, an initiative that explores contemporary art that produces, contributes or provokes public debate. The project involved nine teams around the world that worked for twelve months in their local contexts: Beijing, Cairo, Cape Town, Hamburg, Honk Kong, Mexico City, Mumbai, St. Petersburg and Zurich. In our case, Mexico City was conformed by Helena Chávez Mac Gregor, Cuauhtémoc Medina and the artistic collective Teatro Ojo. For us, one of the questions raised by the Draft project was how to intervene within a public sphere swamped by images of violence that unlike creating a space to produce a collective thought subdues into the effects of its own violence. The local configuration of the public sphere happens as if we are isolated, staring at the catastrophe without any words to say. The images that circulate, normalized, are no longer able to open up our eyes. Its saturation no longer lights up anything. Teatro Ojo’s intervention attempts to produce a different order, sense or formation, by circulating publicly a bunch of different images. Montages trying to turn back to the public sphere –either TV or social network- and appear with the shape of lightning bolts, to establish in its sequence possible new relations. This text is the recounting of the process that lead us to At night, lightening.

In this paper we present some modifications to and extensions of a Wordnet for Bulgarian designed to make it more appropriate for applications in the area of language learning. However, in order to support education, we need to ensure the appropriate selection of sets of synonyms (synsets) from BTB- Wordnet, depending on the education level of the learners, and various types of exercises based on integration of the learning topic and semantic information within Wordnet. For this purpose, our focus is mainly on the combination of the lexemes (lemmas), with their meanings and examples, and the specially designed pictures as illustrations of those meanings within the synsets. We report on our preliminary results.

This paper addresses the classification of images depicting the eruptive activity of Mount Etna, captured by a network of ground-based thermal cameras. The proposed approach utilizes Convolutional Neural Networks (CNNs), focusing on pretrained models. Eight popular pretrained neural networks underwent systematic evaluation, revealing their effectiveness in addressing the classification problem. The experimental results demonstrated that, following a retraining phase with a limited dataset, specific networks such as VGG-16 and AlexNet, achieved an impressive total accuracy of approximately 90%. Notably, VGG-16 and AlexNet emerged as practical choices, exhibiting individual class accuracies exceeding 90%. The case study emphasized the pivotal role of transfer learning, as attempts to solve the classification problem without pretrained networks resulted in unsatisfactory outcomes.

The fish are incorporated with ice to preserve their freshness when sold on the market. Ordinary people can only detect its freshness with some basic freshness knowledge. Therefore, non-destructive fish freshness inspection is an innovative solution to help. This dataset provides a medium to develop a system for non-destructive detection of fish freshness. There are three data variations: sensor data, images, and organoleptic examination. This dataset includes three fish species: mackerel, tilapia, and tuna, using 21 fish of each species. Data generation was carried out for 11 days, where 800 MQ (Metal Oxide) 135 and TGS (Taguchi Gas Sensor) 2602 sensor data and 80 images were generated every day. Organoleptic examinations were carried out using the Indonesian National Standard (SNI) 2729-2013 on six parameters: eyes, gills, body surface mucus, meat, smell, and body textures. This dataset can be used to develop a fish freshness detection system, regression modeling to estimate the deterioration in fish freshness, and standard grouping of freshness classes.

ABSTRACTImpacts of the surrounding land use land cover changes on Suba Sabeta Forest over the past three decades from 1990 to 2020 and associated community perception were assessed employing an integrated approach of Landsat images analysis, household survey, key informants interview and focus group discussion. The study involved collection of both quantitative and qualitative data which were analyzed using descriptive statistics. The results revealed that forests and shrub declined from 9,220 ha and 1335 ha to 2702 ha and 783 ha while settlement and bare land increased from 866 ha and 273 ha to 5,589 ha and 3,978 ha, respectively. Cultivated land increased from 12,162 ha in 1990 to 14,329 ha in 2005 and then declined to 10,811 ha by 2020. Respondents’ responses also indicated a drastic decline in the forest cover related to fuel wood collection (81.6%), settlement (13.8%), cutting trees for construction (3.3%) and expansion of cultivated land (1.3%). According to the respondents poverty (79.08%) and population growth (20.92%) were the underlying causes of the forest cover decline. Responses further revealed disappearance of indigenous plants (biodiversity loss) (73.2%), soil erosion (18%) and decline in agricultural production (8.8%) related to the decline in forest cover. Thus, protection of the remnant forest, reforestation and developing renewable alternative energy sources might help to mitigate further decline in Suba Sebeta Forest cover and associated impacts.

Featured Application: This framework can be applied to evaluate plant density and health in different types of green stormwater infrastructures, agriculture land, and forests. Over the past few decades, there has been a notable surge in interest in green stormwater infrastructure (GSI). This trend is a result of the need to effectively address issues related to runoff, pollution, and the adverse effects of urbanization and impervious surfaces on waterways. Concurrently, umanned aerial vehicles (UAVs) have gained prominence across applications, including photogrammetry, military applications, precision farming, agricultural land, forestry, environmental surveillance, remote-sensing, and infrastructure maintenance. Despite the widespread use of GSI and UAV technologies, there remains a glaring gap in research focused on the evaluation and maintenance of the GSIs using UAV-based imagery. This study aimed to develop an integrated framework to evaluate plant density and health within GSIs using UAV-based imagery. This integrated framework incorporated the UAV (commonly known as a drone), WebOpenDroneMap (WebDOM), ArcMap, PyCharm, and the Canopeo application. The UAV-based images of GSI components, encompassing trees, grass, soil, and unhealthy trees, as well as entire GSIs (e.g., bioretention and green roofs) within the Morgan State University (MSU) campus were collected, processed, and analyzed using this integrated framework. Results indicated that the framework yielded highly accurate predictions of plant density with a high R2 value of 95.8% and lower estimation errors of between 3.9% and 9.7%. Plant density was observed to vary between 63.63% and 75.30% in the GSIs at the MSU campus, potentially attributable to the different types of GSI, varying facility ages, and inadequate maintenance. Normalized difference vegetation index (NDVI) maps and scales of two GSIs were also generated to evaluate plant health. The NDVI and plant density results can be used to suggest where new plants can be added and to provide proper maintenance to achieve proper functions within the GSIs. This study provides a framework for evaluating plant performance within the GSIs using the collected UAV-based imagery. [ABSTRACT FROM AUTHOR]

AbstractThe diagnosis of hysteria, despite being fundamental in the birth of psychiatry, has currently been removed from nosography. This choice speaks of the renunciation by contemporary nosography of understanding psychopathological conditions as structural entities, with internal coherence and meaningfulness – which on the contrary should be reconsidered. Hysteria represents a mirror of social and cultural changes. The metamorphoses throughout history of hysterical symptoms reflect the changing interests of medicine (the greater legitimation of somatic symptoms over psychic ones) and, in general, mirror the increasing importance of images in the contemporary world. Despite its nosographical fragmentation, hysteria continues to be talked about. Phenomenologically, hysteria is described not as a diagnosis but as an existential position, freeing it from gender prejudices. Hysterical persons suffer from a hypo-sufficiency of the self, from a difficulty in feeling from within, which ends up hypertrophying the identity definitions coming from without: the gaze of others, socio-cultural stereotypes, gender models. Visibility therefore takes on a central role, and images become a vehicle to represent oneself – capable of attracting the attention of others and enchanting them. Different powers have been attributed to images throughout the history of thought: that of paralyzing, that of moving to action, that of underlining the contingency of experience. Hysterical persons embody images, generating with their symptoms a world of representations. However, what characterizes hysteria is not the symptom, but the use made of that symptom: it becomes a catalyst for the gaze of others, which allows one to assume an otherwise lacking identity-consistency. For this reason, hysterical persons are not only passively subject to their symptoms, but actively make use of them in interpersonal relations. [ABSTRACT FROM AUTHOR]

This article introduces a relational approach to studying imaginaries of the future, emphasising their significance in comprehending present realities and the ongoing processes that interweave our social fabric. It posits 'imaginaries of the future' as a pivotal sociological concept for understanding the reciprocal social influences and uneven structural dynamics shaping the present. This work engages in a theoretical discourse, spotlighting the role of the future in contemporary social landscapes, while endorsing the suitability of the concept of imaginaries to elucidate how we collectively interlace our present through implicit dialogues with latent, emergent futures and glimpses of radical imagination. In this article, we advocate for sociological research on 'imaginaries', discussing the concept's relevance to sociological theory and research. In addition, we make a case for examining futures as a subject of sociological research. Finally, we propose a conceptual framework for analysing imaginaries of the future from a relational sociological perspective, fostering interdisciplinary dialogue. [ABSTRACT FROM AUTHOR]

Level of detail (LoD)-2 reconstruction is an inevitable task in digital twin-related applications such as disaster management, flood simulation, landslide simulation and solar panel recommendation. However, there is a lack of capable methods that can exploit fine details in RGB imagery and mitigate noise in photogrammetric digital surface models (DSMs). Our investigation is focused on the use of roof planes to achieve a geometrically complete and correct, and topologically consistent LoD-2 building reconstruction. Using UNet with the EfficientNet-B3 backbone, the developed approach starts with jointly predicting building sections and roof planes from the orthorectified RGB imagery and a photogrammetric DSM. The detected sections and planes are then vectorized by employing tree search and simplified with the Douglas Peucker algorithm. Subsequently, height values from the noisy input DSM and the vectorized image-based (and simplified) roof planes are used to derive 3D-planes. Finally, the building model is formed by computing plane intersections as the ridge lines. This study demonstrates that a well-designed depth attention module (DAM), which is the bottleneck of the UNet, can achieve a very good use of both spectral and depth features. The resultant 1-to-n correspondence between building section and roof plane benefits accurate and consistent building model reconstruction. Furthermore, it leads to a superior generalization capability of the proposed method. Experiments with 1437 buildings from the cities Cologne and Braunschweig, Germany, demonstrate the success of the proposed workflow in reconstructing compound buildings with complex roof structures. The achieved geometric mean absolute error (MAE) is 1.06 m and 0.24 m respectively. Comprehensive comparative evaluations showcase the superiority of the approach in terms of geometric completeness and accuracy, and topological consistence with. The improvement over SAT2LOD2 (Gui and Qin, 2021) is 1.12 m in Cologne (data accessible at https://github.com/dlrPHS/GPUB) and 0.47 m in Braunschweig in geometrical MAE. • A holistic method that jointly predicts building sections and roof planes • A novel workflow utilizes roof planes for level of detail (LoD)-2 reconstruction • A well-designed depth attention module (DAM) in a UNet architecture. [ABSTRACT FROM AUTHOR]

In this article, we present methods for the use of eye-tracking in interviews in order to reflect on visual perception and practices of looking as part of an ethnography of the senses. The methods are based on two multi-year ethnographic studies involving eye trackers. In the first one, researchers used mobile eye trackers to study how art museum visitors approach digital image technologies. In the other, they relied on stationary eye trackers to investigate practices on digital image platforms. We discuss how video recordings of participants' eye movements were made and describe the process of conducting ethnographic interviews based on the videos. The eye-tracking interviews can be used 1. to make participants aware of and think about practices of looking; 2. to verbalize in dialogue sensory and interpretative processes regarding museum objects and digital image technologies; and 3. to surface individuals' aesthetic preferences and incorporated knowledge. [ABSTRACT FROM AUTHOR]

In this paper, image-processing techniques are used to observe changes in the direction of the wave spectrum in shallow waters, especially in the surf zone area. This method is based on a time series of pixel brightness in image data. Directional wave spectrum in the surf zone area is determined by using the Bayesian Directional Method. This research uses data from observations in the Hasaki region, Japan. The results of determining the wave direction spectrum in the surf zone using the Bayesian Directional method have showed that the principle direction at the peak frequency is not too influenced by the wave breaking process. Conversely, a broadening of the spreading direction occurs when the wave begins to break over the sandbar and head towards the beach. [ABSTRACT FROM AUTHOR]

Disaster management and the creation of hazard maps are activities that come to minimize the damage caused by disasters through processes to prevent the destruction and degradation of the environment. Floods represent one of the most dangerous disasters and are frequently encountered in different areas of the Earth's surface. The causes of these phenomena can be natural or the result of inappropriate exploitation of human activities. Among the most frequent causes of flooding are heavy rains, storms or melting snow. The present research evaluated remote sensing methods and techniques combined with the science of geographic information systems in the analysis of floods in the Western area of Romania, as well as in the estimation of the areas affected by these floods. The studied area is located near the town of Lugoj in Timis county - Romania. Radar images (SAR) taken from the Sentinel-1A remote sensing system were used to analyze and create maps of the flooded areas. The images are not influenced by weather conditions and can be taken both during the day and at night, which provides a good source of high-resolution datasets. In conclusion, this study can provide answers to the reason for the expansion of floods in the studied area and to a more rigorous planning in order to reduce and manage risks in periods of high flood risk. [ABSTRACT FROM AUTHOR]